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# Deep Learning in Medical Imaging and Medical Image Analysis
## Review and Survey
### Guest Editorial Deep Learning in Medical Imaging Overview and Future Promise of an Exciting New Technique 2016 [[paper]](http://ieeexplore.ieee.org/document/7463094/)
### Overview of Deep Learning in Medical Imaging 2017 [[paper]](https://link.springer.com/article/10.1007/s12194-017-0406-5)
### A Survey on Deep Learning in Medical Image Analysis 2017 [[paper]](http://www.sciencedirect.com/science/article/pii/S1361841517301135)
### Deep Learning Applications in Medical Image Analysis 2017 [[paper]](https://ieeexplore.ieee.org/document/8241753/)
### Deep Learning in Medical Image Analysis 2017 [[paper]](http://www.annualreviews.org/doi/10.1146/annurev-bioeng-071516-044442)
### Deep Learning in Microscopy Image Analysis A Survey 2017 [[paper]](https://ieeexplore.ieee.org/document/8118310/)
### GANs for Medical Image Analysis arXiv 2018 [[paper]](https://arxiv.org/abs/1809.06222)
### Generative Adversarial Network in Medical Imaging: A Review arXiv 2018 [[paper]](https://arxiv.org/abs/1809.07294)
### Deep Learning in Medical Image Registration: A Survey arXiv 2019 [[paper]](https://arxiv.org/abs/1903.02026)
### Deep Learning in Medical Image Registration: A Review arXiv 2019 [[paper]](https://arxiv.org/abs/1912.12318)
### Deep Learning in Medical Ultrasound Analysis A Review Engineering 2019 [[paper]](https://www.sciencedirect.com/science/article/pii/S2095809918301887)
### Deep Learning in Cardiology arXiv 2019 [[paper]](https://arxiv.org/abs/1902.11122)
### Deep learning in Medical Imaging and Radiation Therapy MP 2019 [[paper]](https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13264)
### Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges JDI 2019 [[paper]](https://link.springer.com/article/10.1007/s10278-019-00227-x)
### Embracing Imperfect Datasets A Review of Deep Learning Solutions for Medical Image Segmentation MedIA 2020 [[arXiv paper]](https://arxiv.org/abs/1908.10454) [[MedIA paper]](https://www.sciencedirect.com/science/article/abs/pii/S136184152030058X)
### Machine Learning Techniques for Biomedical Image Segmentation An Overview of Technical Aspects and Introduction to State-of-Art Applications arXiv 2019 [[paper]](https://arxiv.org/abs/1911.02521)
### Deep Neural Network Models for Computational Histopathology A Survey arXiv 2019 [[paper]](https://arxiv.org/abs/1912.12378)
### A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis arXiv 2020 [[paper]](https://arxiv.org/abs/2004.12150)
### State-of-the-Art Deep Learning in Cardiovascular Image Analysis JACC 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1936878X19305753)
### A Review of Deep Learning in Medical Imaging Image Traits Technology Trends Case Studies with Progress Highlights and Future Promises arXiv 2020 [[paper]](https://arxiv.org/abs/2008.09104)
### Review of Artificial Intelligence Techniques in Imaging Data Acquisition Segmentation and Diagnosis for COVID-19 IEEE RBME 2020 [[paper]](https://ieeexplore.ieee.org/document/9069255)
### Model-Based and Data-Driven Strategies in Medical Image Computing IEEE Proceedings 2020 [[paper]](https://ieeexplore.ieee.org/document/8867900) [[arXiv paper]](https://arxiv.org/abs/1909.10391)
### Deep Learning Based Brain Tumor Segmentation A Survey arXiv 2020 [[paper]](https://arxiv.org/abs/2007.09479)
### A Review Deep Learning for Medical Image Segmentation Using Multi-modality Fusion arXiv 2020 [[paper]](https://arxiv.org/abs/2004.10664)
### Medical Instrument Detection in Ultrasound-Guided Interventions A Review arXiv 2020 [[paper]](https://arxiv.org/abs/2007.04807)
### A Review of Deep Learning in Medical Imaging Image Traits Technology Trends Case Studies with Progress Highlights and Future Promises arXiv 2020 [[paper]]()
### Medical Image Segmentation Using Deep Learning A Survey arXiv 2020 [[paper]](https://arxiv.org/abs/2009.13120)
### Learning-based Algorithms for Vessel Tracking A Review arXiv 2020 [[paper]]()
### Deep Learning for Cardiac Image Segmentation A Review FCVM 2020 [[paper]](https://www.frontiersin.org/articles/10.3389/fcvm.2020.00025/full)
### Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology Circulation 2020 [[paper]](https://www.ahajournals.org/doi/full/10.1161/CIRCEP.119.007952)
### Overview of the Whole Heart and Heart Chamber Segmentation Methods CET 2020 [[paper]](https://link.springer.com/article/10.1007/s13239-020-00494-8)
### Deep Learning for Chest X-ray Analysis A Survey arXiv 2021 [[paper]](https://arxiv.org/abs/2103.08700)
### Multi-Modality Cardiac Image Computing A Survey arXiv 2022 [[paper]]()
### Nuclei & Glands Instance Segmentation in Histology Images A Narrative Review arXiv 2022 [[paper]]()

## Datasets
### Development of a Digital Image Database for Chest Radiographs with and without a Lung Nodule AJR 2000
"Chest Radiographs", "the JSRT database"
### Segmentation of Anatomical Structures in Chest Radiographs Using Supervised Methods A Comparative Study on a Public Database MedIA 2006
"Chest Radiographs", "the SCR dataset (ground-truth segmentation masks) for the JSRT database (X-ray images)"
### ChestX-ray8 Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases CVPR 2017 [[dataset]](https://nihcc.app.box.com/v/ChestXray-NIHCC)
"Chest Radiographs"
### KiTS 2019 [[dataset]](https://github.com/neheller/kits19)
"300 Abdomen CT scans for kidney and tumor segmentation"
### CHD_Segmentation [[dataset]](https://github.com/XiaoweiXu/Whole-heart-and-great-vessel-segmentation-of-chd_segmentation/tree/master)
"68 CT images with labels. The label includes left ventricle, right ventricle, left atrium, right atrium, myocardium, aorta, and pulmonary artery."
### Skin Lesion Analysis Toward Melanoma Detection 2018 A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) arXiv 2019
### ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection arXiv 2017 [[paper]](https://arxiv.org/abs/1703.00523)
"ISIC2016", "ISIC2017", "ISIC2018", "ISIC2019"
### VerSe A Vertebrae Labelling and Segmentation Benchmark arXiv 2020 [[paper]](https://arxiv.org/abs/2001.09193)
"VerSe"
### A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology IEEE TMI 2017 [[paper]](https://ieeexplore.ieee.org/document/7872382)
### A Multi-Organ Nucleus Segmentation Challenge IEEE TMI 2020 [[paper]]()
"MoNuSeg"
### Deep Learning to Segment Pelvic Bones Large-scale CT Datasets and Baseline Models arXiv 2020 [[paper]]()
"CTPelvic1K"
### RibSeg v2 A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction arXiv 2022 [[paper]]()
"RibSeg"

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# Computed Tomography (CT)
## 2022
### Learning Topological Interactions for Multi-Class Medical Image Segmentation ECCV Oral 2022 [[paper]](https://arxiv.org/abs/2207.09654) [[code]](https://github.com/TopoXLab/TopoInteraction)

## 2015
### 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data MICCAI 2015 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-24553-9_69)

## 2016
### An Artificial Agent for Anatomical Landmark Detection in Medical Images MICCAI 2016 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46726-9_27)
"deep reinforcement learning", "anatomical landmark detection"
### Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields MICCAI 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_48)
"CRF"
### Low-dose CT Denoising with Convolutional Neural Network [[paper]](https://arxiv.org/abs/1610.00321)
### Low-Dose CT via Deep Neural Network [[paper]](https://arxiv.org/abs/1609.08508)
### Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks [[paper]](https://ieeexplore.ieee.org/document/7422783/)
### Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation IEEE TMI 2016 [[paper]](https://ieeexplore.ieee.org/document/7279156)

## 2017
### Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [[paper]](https://arxiv.org/abs/1708.00961)
### Automatic Liver Segmentation Using an Adversarial Image-to-Image Network MICCAI 2017 [[paper]](https://arxiv.org/abs/1707.08037)
### Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network [[paper]](https://arxiv.org/abs/1708.06453)
### Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT [[paper]](https://arxiv.org/abs/1708.08333)
### Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image [[paepr]](https://arxiv.org/abs/1709.02073)
### A Self-aware Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation [[paper]](https://arxiv.org/abs/1709.02764)
### DeepLesion Automated Deep Mining Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations [[paper]](https://arxiv.org/abs/1710.01766)
### Unsupervised End-to-end Learning for Deformable Medical Image Registration [[paper]](https://arxiv.org/abs/1711.08608)
### DeepLung 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification [[paper]](https://arxiv.org/abs/1709.05538)
### CT Image Denoising with Perceptive Deep Neural Networks [[paper]](https://arxiv.org/abs/1702.07019)
### Improving Low-Dose CT Image Using Residual Convolutional Network [[paper]](http://ieeexplore.ieee.org/document/8082505/)
### Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) [[paper]](https://ieeexplore.ieee.org/document/7947200/)
### Stacked Competitive Networks for Noise Reduction in Low-dose CT [[paper]](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190069)
### Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network [[paper]](https://arxiv.org/abs/1711.08324)
### Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-42999-1_4)
### Robust Multi-scale Anatomical Landmark Detection in Incomplete 3D-CT Data [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66182-7_23)
### Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans TPAMI 2017 [[paper]](https://ieeexplore.ieee.org/document/8187667/)
### 3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images MedIA 2017 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841517300725)
"deep supervision mechanism"
### Generative Adversarial Networks for Noise Reduction in Low-Dose CT IEEE TMI 2017 [[paper]](https://ieeexplore.ieee.org/document/7934380)

## 2018
### A Two-stage 3D Unet Framework for Multi-class Segmentation on Full Resolution Image arXiv 2018[[paper]](https://arxiv.org/abs/1804.04341)
### DeepLung Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification [[paper]](https://arxiv.org/abs/1801.09555)
### Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans [[paper]](https://arxiv.org/abs/1801.08599)
### Attention U-Net Learning Where to Look for the Pancreas [[paper]](https://arxiv.org/abs/1804.03999)
### 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network [[paper]](https://arxiv.org/abs/1802.05656)
### Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network [[paper]](https://ieeexplore.ieee.org/document/8332971/)
### Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising [[paper]](https://arxiv.org/abs/1805.00587)
### Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data MedIA 2018 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841518304092)
### Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images Arxiv 2018 [[paper]](https://arxiv.org/abs/1807.02908)
"reinforcement learning", "anatomical landmark localization", "aortic valve". "left atrial appendage"
### Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation [[paper]](https://arxiv.org/abs/1808.00739)
### Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network CVPR 2018 [[paper]](https://arxiv.org/abs/1802.09655)
### AnatomyNet Deep 3D Squeeze-and-excitation U-Nets for Fast and Fully Automated Whole-volume Anatomical Segmentation Medical Physics 2018 [[paper]](https://arxiv.org/abs/1808.05238)
### DeepEM Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection MICCAI 2018 [[paper]](https://arxiv.org/abs/1805.05373)
### Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks 2018 [[paper]](https://arxiv.org/abs/1809.02268)
### Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior [[paper]](https://arxiv.org/abs/1804.01307)
### Deep Learning Based Rib Centerline Extraction and Labeling [[paper]](https://arxiv.org/abs/1809.07082)
### Liver Lesion Detection from Weakly-Labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector MICCAI 2018 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-00934-2_77)
### CFUN Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation 2018 [[paper]](https://arxiv.org/abs/1812.04914)
### Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database CVPR 2018 [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/html/Yan_Deep_Lesion_Graphs_CVPR_2018_paper.html)
### 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas CR 2018 [[paper]](http://cancerres.aacrjournals.org/content/78/24/6881.short)
### (AH-Net) 3D Anisotropic Hybrid Network Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes MICCAI 2018 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-00934-2_94)
"liver and liver tumor segmentation from a Computed Tomography volume", "lesion detection from a Digital Breast Tomosynthesis volume"
### 3D U-JAPA-Net Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation MICCAI 2018 [[paper]]()
### A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation MICCAI 2018 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-00937-3_48)
### Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs IJCARS 2018 [[paper]](https://link.springer.com/article/10.1007/s11548-018-1884-6)

## 2019
### 3DFPN-HS2 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection MICCAI 2019 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_57)
### A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography IEEE TMI 2019 [[paper]](https://ieeexplore.ieee.org/document/8550784)
### Abdominal Multi-organ Segmentation with Organ-attention Networks and Statistical Fusion MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518302524)
### Attention Gated Networks Learning to Leverage Salient Regions in Medical Images MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841518306133)
### Automated Coronary Artery Atherosclerosis Detection and Weakly Supervised Localization on Coronary CT Angiography with a Deep 3-Dimensional Convolutional Neural Network arXiv 2019 [[paper]](https://arxiv.org/abs/1911.13219) [[CMIG paper]](https://www.sciencedirect.com/science/article/pii/S0895611120300240)
### Automated Design of Deep Learning Methods for Biomedical Image Segmentation arXiv 2019 [[paper]](https://arxiv.org/abs/1904.08128)
### Combined Analysis of Coronary Arteries and the Left Ventricular Myocardium in Cardiac CT Angiography for Detection of Patients with Functionally Significant Stenosis arXiv 2019 [[paper]](https://arxiv.org/abs/1911.04940)
### Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-based Orientation Classifier MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518308491) [[arXiv paper]](https://arxiv.org/abs/1810.03143)
### Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics MICCAI 2019 [[paper]](https://arxiv.org/abs/1912.06075)
### Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans arXiv 2019 [[paper]](https://arxiv.org/abs/1912.06417)
### Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT IEEE TMI 2019 [[paper]](https://ieeexplore.ieee.org/document/8643342)
### Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography MICCAI 2019 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_52)
### Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network IEEE TMI 2019 [[paper]](https://ieeexplore.ieee.org/document/8625393)
### Motion Artifact Recognition and Quantification in Coronary CT Angiography Using Convolutional Neural Networks MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518308624)
### Motion Estimation and Correction in Cardiac CT Angiography Images Using Convolutional Neural Networks CMIG 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0895611119300515)

## 2020
### 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT MICCAI 2020 [[paper]](https://arxiv.org/abs/2010.03739)
### Bounding Maps for Universal Lesion Detection arXiv 2020 [[paper]](https://arxiv.org/abs/2007.09383)
### C2FNAS Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation CVPR 2020 [[paper]](https://arxiv.org/abs/1912.09628)
### Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation MICCAI 2020 [[paper]](https://arxiv.org/abs/2007.05393) [[code]](https://github.com/ShawnBIT/Brain-Midline-Detection)
### CPR-GCN Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries CVPR 2020 [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_CPR-GCN_Conditional_Partial-Residual_Graph_Convolutional_Network_in_Automated_Anatomical_Labeling_CVPR_2020_paper.html)
### Deep Distance Transform for Tubular Structure Segmentation in CT Scans CVPR 2020 [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.html)
""
### Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography IEEE TMI 2020 [[paper]](https://ieeexplore.ieee.org/document/8896989)
### Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images arXiv 2020 [[paper]](https://arxiv.org/abs/2009.07469)
### Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images arXiv 2020 [[paper]](https://arxiv.org/abs/2002.04207)
"textures and edge information"
### Going to Extremes Weakly Supervised Medical Image Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2009.11988)
### Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling MLMI 2020 [[paper]]()
### Learning Metal Artifact Reduction in Cardiac CT Images with Moving Pacemakers MedIA 2020 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841520300220)
### Modified U-Net (mU-Net) with Incorporation of Object-dependent High Level Features for Improved Liver and Liver-tumor Segmentation in CT Images IEEE TMI 2020 [[paper]]()
### Multi-resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans arXiv 2020 [[paper]](https://arxiv.org/abs/2010.00925)
"improvement of CNN-based Orientation Classifier (vessel tracker)"
### Multi-view Spatial Aggregation Framework for Joint Localization and Segmentation of Organs at Risk in Head and Neck CT Images IEEE TMI 2020 [[paper]]()
### One Click Lesion RECIST Measurement and Segmentation on CT Scans arXiv 2020 [[paper]](https://arxiv.org/abs/2007.11087)
### PGL Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2011.12640)
### RA-UNet A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans 2020
### Rapid Vessel Segmentation and Reconstruction of Head and Neck Angiograms Using 3D Convolutional Neural Network NC 2020 [[paper]](https://www.nature.com/articles/s41467-020-18606-2)
### SenseCare A Research Platform for Medical Image Informatics and Interactive 3D Visualization arXiv 2020 [[paper]](https://arxiv.org/abs/2004.07031)
### TopNet Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling MICCAI 2020 [[paper]](https://arxiv.org/abs/2009.08674)
### TripletUNet Multi-Task U-Net with Online Voxel-Wise Learning for Precise CT Prostate Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2005.07462)
### UXNet Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2009.07501)

## 2021
### Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss arXiv 2021 [[paper]](https://arxiv.org/abs/2102.01897)
### CoTr Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation arXiv 2021 [[paper]](https://arxiv.org/abs/2103.03024)
### Swin-Unet Unet-like Pure Transformer for Medical Image Segmentation arXiv 2021 [[paper]](https://arxiv.org/abs/2105.05537)
### Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement arXiv 2021 [[paper]](https://arxiv.org/abs/2102.01315)

## 2022
### Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers arXiv 2022 [[paper]]()
### Boundary-Aware Network for Abdominal Multi-Organ Segmentation arXiv 2022 [[paper]]()
### Boundary-Aware Network for Kidney Parsing arXiv 2022 [[paper]]()

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# Magnetic Resonance Imaging (MRI)
## 2022
### (RefSeg) Online Reflective Learning for Robust Medical Image Segmentation MICCAI 2022 [[paper]](https://arxiv.org/abs/2207.00476)

## 2015
### Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation MICCAI 2015 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_1)

## 2016
### Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-55050-3_8)
### Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks MICCAI 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_51)
"CRF"
### Regressing Heatmaps for Multiple Landmark Localization Using CNNs MICCAI 2016 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46723-8_27)
"Multiple Landmark Localization"

## 2017
### SegAN Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[paper]](https://arxiv.org/abs/1706.01805)
### Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images [[paper]](https://arxiv.org/abs/1708.01141)
### Deep MR to CT Synthesis using Unpaired Data [[paper]](https://arxiv.org/abs/1708.01155)
### Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT [[paper]](https://arxiv.org/abs/1708.00983)
### 3D Fully Convolutional Networks for Subcortical Segmentation in MRI A Large-scale Study [[paper]](http://www.sciencedirect.com/science/article/pii/S1053811917303324) [[code]](https://github.com/josedolz/LiviaNET)
### 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation [[paper]](https://arxiv.org/abs/1707.09813)
### Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
### Deep Generative Adversarial Networks for Compressed Sensing Automates MRI [[paper]](https://arxiv.org/abs/1706.00051)
### Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation [[paper]](https://arxiv.org/abs/1708.09300)
### Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks [[paper]](https://arxiv.org/abs/1709.00382)
### Deep Learning with Domain Adaptation for Accelerated Projection Reconstruction MR [[paper]](https://arxiv.org/abs/1703.01135)
### A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [[paper]](https://ieeexplore.ieee.org/document/8067520/)
### Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1709.00753)
### Learning a Variational Network for Reconstruction of Accelerated MRI Data [[paper]](https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.26977)
### A Parallel MR Imaging Method Using Multilayer Perceptron [[paper]](https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.12600)
### A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [[paper]](https://ieeexplore.ieee.org/document/8067520/)
### Image Reconstruction by Domain Transform Manifold Learning [[paper]](https://arxiv.org/abs/1704.08841)
### Human-level CMR Image Analysis with Deep Fully Convolutional Networks [[paper]](https://arxiv.org/abs/1710.09289)
### A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_79)
"CRF"
### Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_49)
"CRF"
### Medical Image Synthesis with Context-aware Generative Adversarial Networks MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-66179-7_48) [[arXiv paper]](https://arxiv.org/abs/1612.05362)

## 2018
### Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks [[paper]](https://arxiv.org/abs/1801.02728)
### 3D Multi-scale FCN with Random Modality Voxel Dropout Learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841518300136)
### Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network [[paper]](https://arxiv.org/abs/1803.01417)
### Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks [[paper]](https://arxiv.org/abs/1804.00432)
### k-Space Deep Learning for Accelerated MRI [[paper]](https://arxiv.org/abs/1805.03779)
### Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation [[paper]](https://arxiv.org/abs/1808.01200)
### Deformable Image Registration Using a Cue-Aware Deep Regression Network TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8331111/)
### Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8067513/)
### 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes MICCAI 2018 [[paper]](https://arxiv.org/abs/1809.00076)
"focal loss", "Exponential Logarithmic Loss"
### Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks 2018 [[paper]](https://link.springer.com/chapter/10.1007/978-3-662-56537-7_89)
### An Unsupervised Learning Model for Deformable Medical Image Registration CVPR 2018 [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/html/Balakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.html)
### VoxelMorph: A Learning Framework for Deformable Medical Image Registration IEEE TMI 2018 [[paper]](https://arxiv.org/abs/1809.05231)
### Direct Delineation of Myocardial Infarction without Contrast Agents Using a Joint Motion Feature Learning Architecture MedIA 2018 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518306960)
### Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation IEEE TMI 2018 [[paper]](http://ieeexplore.ieee.org/document/8051114/)
### Towards MR-Only Radiotherapy Treatment Planning: Synthetic CT Generation Using Multi-view Deep Convolutional Neural Networks MICCAI 2018 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-00928-1_33)
### Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN DLMIA 2018 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-00889-5_20) [[arXiv paper]](https://arxiv.org/abs/1809.04536)

## 2019
### A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation MICCAI 2019 [[paper]](https://arxiv.org/abs/1906.06148) [[code]](https://github.com/RobinBruegger/PartiallyReversibleUnet)
### Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network IEEE TMI 2019 [[paper]](https://ieeexplore.ieee.org/document/8447517)

## 2020
### Automated Intracranial Artery Labeling Using a Graph Neural Network and Hierarchical Refinement MICCAI 2020 [[paper]](https://arxiv.org/abs/2007.14472)
### Brain Tumor Segmentation Using 3D-CNNs with Uncertainty Estimation arXiv 2020 [[paper]](https://arxiv.org/abs/2009.12188)
### CA-Net Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2009.10549)
### (CANet) CANet Context Aware Network for 3D Brain Tumor Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2007.07788)
### Cardiac Segmentation with Strong Anatomical Guarantees arXiv 2020 [[paper]](https://arxiv.org/abs/2006.08825)
### CS2-Net Deep Learning Segmentation of Curvilinear Structures in Medical Imaging arXiv 2020 [[paper]](https://arxiv.org/abs/2010.07486)
### Deep Morphological Simplification Network MS-Net for Guided Registration of Brain Magnetic Resonance Images PR 2019 [[paper]](https://www.sciencedirect.com/science/article/pii/S0031320319304716) [[paper]](https://arxiv.org/abs/1902.02342)
### Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network arXiv 2020 [[paper]](https://arxiv.org/abs/2009.12111)
### Knowledge Distillation for Brain Tumor Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2002.03688)
### MS-Net Multi-site Network for Improving Prostate Segmentation with Heterogeneous MRI Data IEEE TMI 2020 [[paper]]()
### Optimization for Medical Image Segmentation Theory and Practice When Evaluating with Dice Score or Jaccard Index IEEE TMI 2020 [[paper]]()
### (AsynDGAN) Synthetic Learning Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data CVPR 2020 [[paper]](https://arxiv.org/abs/2006.00080)
"AsynDGAN is comprised of one central generator and multiple distributed discriminators located in different medical entities."
### Two-Stage Cascaded U-Net 1st Place Solution to BraTS Challenge 2019 Segmentation Task BrainLes 2019 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-46640-4_22)
### UNet++ Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation IEEE TMI 2020 [[paper]]()
### ψ-Net Stacking Densely Convolutional LSTMs for Sub-cortical Brain Structure Segmentation IEEE TMI 2020 [[paper]]()

## 2021
### TransBTS Multimodal Brain Tumor Segmentation Using Transformer arXiv 2021 [[paper]](https://arxiv.org/abs/2103.04430) [[PyTorch code]](https://github.com/Wenxuan-1119/TransBTS)

## 2022
### Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis arXiv 2022 [[paper]]()

----------------------------------------------------------------------------------------------------------------------------------------
# Ultrasound (US)
## 2015
### Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks MICCAI 2015 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-24553-9_62)
### Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks IEEE JBHI 2015 [[paper]](https://ieeexplore.ieee.org/document/7090943)

## 2016
### Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset [[paper]](https://www.sciencedirect.com/science/article/pii/S0925231216002344)
### Real-time Detection and Localisation of Fetal Standard Scan Planes in 2D Freehand Ultrasound 2016 [[paper]]()
### Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_24)
### Describing Ultrasound Video Content Using Deep Convolutional Neural Networks 2016 [[paper]](http://ieeexplore.ieee.org/document/7493384/)

## 2017
### Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning [[paepr]](https://arxiv.org/abs/1706.00712)
### Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1707.05392)
### Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning [[paper]](https://arxiv.org/abs/1712.07881)
### Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation [[paper]](http://ieeexplore.ieee.org/document/8051114/)
### Hough-CNN Deep learning for segmentation of deep brain regions in MRI and ultrasound CVIU 2017 [[paper]](https://www.sciencedirect.com/science/article/pii/S1077314217300620)
### Cascaded Fully Convolutional Networks for Automatic Prenatal Ultrasound Image Segmentation 2017 [[paper]](http://ieeexplore.ieee.org/document/7950607/)
### Ultrasound Standard Plane Detection Using a Composite Neural Network Framework 2017 [[paper]](http://ieeexplore.ieee.org/document/7890445/)
### CNN-based Estimation of Abdominal Circumference from Ultrasound Images 2017 [[paper]](https://arxiv.org/abs/1702.02741)
### Ultrasound Image-based Thyroid Nodule Automatic Segmentation Using Convolutional Neural Networks IJCARS 2017 [[paper]]
"thyroid"
### SonoNet Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound IEEE TMI 2017 [[paper]](https://ieeexplore.ieee.org/document/7974824) [[arXiv paper]](https://arxiv.org/abs/1612.05601)

## 2018
### A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification IEEE TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8372445/)
### Adversarial Image Registration with Application for MR and TRUS Image Fusion 2018 [[paper]](https://arxiv.org/abs/1804.11024)
### Attention-Gated Networks for Improving Ultrasound Scan Plane Detection 2018 [[paper]](https://openreview.net/forum?id=BJtn7-3sM)
### Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting [[paper]](https://ieeexplore.ieee.org/document/7927411/)
### Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound [[paepr]](https://openreview.net/forum?id=r1ZGQW2if)
### Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging 2018 [[paper]](https://arxiv.org/abs/1805.10737)
### Fast Multiple Landmark Localisation Using a Patch-based Iterative Network MICCAI 2018 [[paper]](https://arxiv.org/abs/1806.06987) [[TF code]](https://github.com/yuanwei1989/landmark-detection)
### Fully-automated Alignment of 3D Fetal Brain Ultrasound to a Canonical Reference Space Using Multi-task Learning MedIA 2018 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518300306)
### Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model 2018 [[paper]](https://ieeexplore.ieee.org/document/8051098/)
### High Frame-rate Cardiac Ultrasound Imaging with Deep Learning MICCAI 2018 [[paper]](https://arxiv.org/abs/1808.07823)
### High Quality Ultrasonic Multi-line Transmission through Deep Learning MICCAI 2018 [[paper]](https://arxiv.org/abs/1808.07819)
### Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks [[paper]](https://arxiv.org/abs/1804.09102)
### Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks CR 2018 [[paper]](http://cancerres.aacrjournals.org/content/78/17/5135.short)
### Less is More Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images 2018 [[paper]](https://arxiv.org/abs/1805.10376)
### Multi-task SonoEyeNet Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps MICCAI 2018 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-00928-1_98)
### Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network 2018 [[paper]](https://arxiv.org/abs/1806.07486)
### Weakly Supervised Localisation for Fetal Ultrasound Images DLMIAW 2018 [[paper]](https://arxiv.org/abs/1808.00793)

## 2019
### Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation IEEE TMI 2018 [[paper]](Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation)
### Automated Detection and Classification of Thyroid Nodules in Ultrasound Images Using Clinical-knowledge-guided Convolutional Neural Networks MedIA 2019 [[paper]]()
"thyroid"

## 2020
### Contrastive Rendering for Ultrasound Image Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2010.04928)
### Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network IEEE TBME 2020 [[paper]](https://ieeexplore.ieee.org/document/8698332)
### Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging IEEE TMI 2020 [[paper]](https://ieeexplore.ieee.org/document/8896024)
### Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images MICCAI 2020 [[paper]](https://arxiv.org/abs/2007.10732) [[code]](https://github.com/kleinzcy/SASSnet)
### Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis IEEE TMI 2020 [[paper]](https://arxiv.org/abs/2008.08698) [[code]](https://bitbucket.org/JianboJiao/ssus2mri/src)

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# X-ray
## 2015
### Deep Learning and Structured Prediction for the Segmentation of Mass in Mamograms MICCAI 2015 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-24553-9_74)

## 2016
### Learning to Read Chest X-Rays Recurrent Neural Cascade Model for Automated Image Annotation 2016 [[paper]](https://arxiv.org/abs/1603.08486)

## 2017
### Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks DLMIA 2017 [[paper]](https://arxiv.org/abs/1708.00710)
### Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks [[paper]](https://arxiv.org/abs/1705.09850)
### Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks 2017 [[paper]](https://arxiv.org/abs/1712.05053)
"reimplement this recently", "segmentation data for normalization was done"
### Cascade of Multi-scale Convolutional Neural Networks for Bone Suppression of Chest Radiographs in Gradient Domain 2017 [[paper]](http://www.sciencedirect.com/science/article/pii/S1361841516301529)
### CheXNet Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning 2017 [[paper]](https://arxiv.org/abs/1711.05225)
### Adversarial Deep Structural Networks for Mammographic Mass Segmentation MICCAI 2017 [[paper]](https://arxiv.org/abs/1612.05970)
### Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66179-7_69)
### A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-67558-9_20)
### High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks 2017 [[paper]](https://arxiv.org/abs/1703.07047)
### Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning TMI 2017 [[paper]](https://ieeexplore.ieee.org/document/8032490/)
### Deep Learning for Automated Skeletal Bone Age Assessment in X-ray Images MedIA 2017
"focus on this recently (20181001)"

## 2018
### SCAN Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays [[paper]](https://openreview.net/forum?id=HJ1RffhjM)
### Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs IEEE TMI 2018 [[TMI paper]](https://ieeexplore.ieee.org/document/8302848/) [[ArXiv paper]](https://arxiv.org/abs/1701.08816)
### Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation 2018 [[paper]](https://arxiv.org/abs/1806.00600)
### LF-SegNet A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs 2018 [[paper]](https://link.springer.com/article/10.1007/s11277-018-5702-9)
### Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks 2018 [[paper]]()
### Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification 2018 [[paper]](https://arxiv.org/abs/1803.02315)
### Breast Mass Segmentation and Shape Classification in Mammograms Using Deep Neural Networks [[paper]](https://arxiv.org/abs/1809.01687)
"conditional generative adversarial networks", "INbreast", "digital database for screening mammography (DDSM)"
### Medical Image Description Using Multi-task-loss CNN 2016 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46976-8_13)
### Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification MICCAI 2018 [[paper]](https://arxiv.org/abs/1805.10207)
### Benign and malignant breast tumors classification based on region growing and CNN segmentation ESA 2015 [[paper]](https://www.sciencedirect.com/science/article/pii/S0957417414005594)
### Adversarial Deep Structured Nets for Mass Segmentation from Mammograms ISBI 2018 [[paper]](https://arxiv.org/abs/1710.09288)
### Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net MICCAI 2018 [[paper]](https://arxiv.org/abs/1808.08885)
### Thoracic Disease Identification and Localization with Limited Supervision CVPR 2018 [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers_backup/Li_Thoracic_Disease_Identification_CVPR_2018_paper.pdf)
### Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions 2018 [[paper]](https://arxiv.org/abs/1803.07703)
### Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning CBM 2018 [[paper]](https://www.sciencedirect.com/science/article/pii/S0010482518300799)
### Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder RAMBO 2018 [[paper]](https://arxiv.org/abs/1810.02113)

## 2019
### Accurate Automated Cobb Angles Estimation Using Multi-view Extrapolation Net MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841519300775)
### Learning to Detect Chest Radiographs Containing Pulmonary Lesions Using Visual Attention Networks MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841518304997)
### When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification IEEE ISBI 2019 [[paper]](https://ieeexplore.ieee.org/document/8759510)

## 2020
### High-resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors IEEE TMI 2020 [[paper]]()
### Image-to-Images Translation for Multi-task Organ Segmentation and Bone Suppression in Chest X-ray Radiography IEEE TMI 2020 [[paper]]()
### Vertebra-focused Landmark Detection for Scoliosis Assessment IEEE ISBI 2020 [[paper]](https://arxiv.org/abs/2001.03187)

## 2021
### Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease arXiv 2021 [[paper]]()
### Seg4Reg+ Consistency Learning between Spine Segmentation and Cobb Angle Regression MICCAI 2021 [[paper]]()

----------------------------------------------------------------------------------------------------------------------------------------

# Positron Emission Tomography (PET)
## 2017
### Combo Loss Handling Input and Output Imbalance in Multi-Organ Segmentation arXiv 2018 [[paper]](https://arxiv.org/abs/1805.02798)
### Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results arXiv 2017 [[paper]](https://arxiv.org/abs/1707.09585)

## 2018
### Iterative PET Image Reconstruction Using Convolutional Neural Network Representation IEEE TMI 2018 [[paper]](https://ieeexplore.ieee.org/document/8463596)
### PET Image Reconstruction Using Deep Image Prior IEEE TMI 2018 [[paper]](https://ieeexplore.ieee.org/document/8581448)

## 2019
### Cross-modality Synthesis from CT to PET Using FCN and GAN Networks for Improved Automated Lesion Detection ENGAPPAI 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0952197618302513)

----------------------------------------------------------------------------------------------------------------------------------------
# Funduscopy
## 2016
### DeepVessel Retinal Vessel Segmentation via Deep Learning and Conditional Random Field MICCAI 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_16)
"CRF"

## 2017
### Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1706.09318) [[Keras+TF code]](https://bitbucket.org/woalsdnd/v-gan)
### Towards Adversarial Retinal Image Synthesis arXiv 2017 [[paper]](https://arxiv.org/abs/1701.08974) [[code]](https://github.com/costapt/vess2ret)

## 2018
### End-to-End Adversarial Retinal Image Synthesis IEEE TMI 2018 [[paper]](https://ieeexplore.ieee.org/document/8055572) [[code]](https://github.com/costapt/adversarial_retinal_synthesis)
### Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation TMI 2018 [[paper]](http://ieeexplore.ieee.org/document/8252743/)
### Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8341481/)

## 2019
### CE-Net: Context Encoder Network for 2D Medical Image Segmentation IEEE TMI 2019 [[paper]](https://ieeexplore.ieee.org/document/8662594)
### Deep Vessel Segmentation by Learning Graphical Connectivity MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841519300982) [[TF code]](https://github.com/syshin1014/VGN)

## 2020
### Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation arXiv 2020 [[paper]](https://arxiv.org/abs/2005.07476)
"IVUS images are similar to Eye Fundus Images."

----------------------------------------------------------------------------------------------------------------------------------------
#  Microscopy
## 2016
### Stain Normalization Using Sparse AutoEncoders (StaNoSA) Application to Digital Pathology [[paper]](http://www.sciencedirect.com/science/article/pii/S0895611116300404)
### Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images IEEE TMI 2016 [[paper]](http://ieeexplore.ieee.org/document/7163353/)

## 2017
### Adversarial Image Alignment and Interpolation [[paper]](https://arxiv.org/abs/1707.00067)
### CNN Cascades for Segmenting Whole Slide Images of the Kidney [[paper]](https://arxiv.org/abs/1708.00251)
### Learning to Segment Breast Biopsy Whole Slide Images [[paper]](https://arxiv.org/abs/1709.02554)
### SFCN-OPI Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction [[paper]](https://arxiv.org/abs/1712.08297)
### MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network CVPR 2017 [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_MDNet_A_Semantically_CVPR_2017_paper.pdf)

## 2018
### Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification ICIAR 2018 [[paper]](https://arxiv.org/abs/1802.00931)
### Cancer Metastasis Detection With Neural Conditional Random Field MIDL 2018 [[paper]](https://arxiv.org/abs/1806.07064)
### DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks MedIA 2018 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841517301834)

## 2019
### Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation MICCAI 2019 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-030-32245-8_12) [[arXiv paper]](https://arxiv.org/abs/1909.11524)
### HoVer-Net Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841519301045)
### Weakly supervised mitosis detection in breast histopathology images using concentric loss MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S1361841519300118)

## 2020
### Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation MICCAI 2020 [[paper]](https://arxiv.org/abs/2007.10787)
### MultiStar Instance Segmentation of Overlapping Objects with Star-convex Polygons arXiv 2020 [[paper]]()
### Nucleus Segmentation Across Imaging Experiments the 2018 Data Science Bowl NM 2020 [[paper]]()
### Red Blood Cell Segmentation with Overlapping Cell Separation and Classification on Imbalanced Dataset arXiv 2020 [[paper]]()

## 2022
### Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images MICCAI 2022 [[paper]]()
### Region-guided CycleGANs for Stain Transfer in Whole Slide Images arXiv 2022 [[paper]]()

----------------------------------------------------------------------------------------------------------------------------------------
# Colonoscopy
## 2016
### Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning TMI 2016 [[papr]](http://ieeexplore.ieee.org/document/7426826/)

## 2018
### Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional Network [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-73603-7_32)

----------------------------------------------------------------------------------------------------------------------------------------
# OCT
## 2017
### Cystoid Macular Edema Segmentation of Optical Coherence Tomography Images Using Fully Convolutional Neural Networks and Fully Connected CRFs 2017 [[paper]](https://arxiv.org/abs/1709.05324)

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# Dermoscopy
## 2016
### Automatic Melanoma Detection via Multi-scale Lesion-biased Representation and Joint Reverse Classification IEEE ISBI 2016 [[paepr]](https://ieeexplore.ieee.org/document/7493447/)
### Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector [[paper]](https://ieeexplore.ieee.org/document/7950524/)
### Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification [[paper]](https://ieeexplore.ieee.org/document/7493447/)

## 2017
### Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks IEEE TMI 2017 [[paper]](http://ieeexplore.ieee.org/document/7792699/)
### Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance [[paper]](http://ieeexplore.ieee.org/document/7903636/)
"Jaccard distance on one hand, is similar to the known Dice overlap coefficient (also a novel loss function in V-Net), on the other hand, in the above paper, is a novel loss function suitable for binary class segmentation task. obviously, Jaccard distance is similar to IoU (intersection over union), a strict metric in object/semantic segmentation in computer vision."
### Investigating deep side layers for skin lesion segmentation [[paper]](https://ieeexplore.ieee.org/document/7950514/)
### Skin Lesion Segmentation via Deep RefineNet [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-67558-9_35)
### Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks [[paper]](https://ieeexplore.ieee.org/document/8239798/)
### Segmentation of dermoscopy images based on fully convolutional neural network [[paper]](https://ieeexplore.ieee.org/document/8296578/)
### Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks [[paper]](https://arxiv.org/abs/1711.10449)
"Multi-class (classification and segmentation)"
### Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks [[paper]](https://ieeexplore.ieee.org/document/8239798/)
### Dermoscopic Image Segmentation via Multi-Stage Fully Convolutional Networks [[paper]](http://ieeexplore.ieee.org/document/7942129/)
### Skin Melanoma Segmentation Using Recurrent and Convolutional Neural Networks IEEE ISBI 2017 [[paper]](https://ieeexplore.ieee.org/document/7950522/)
### Skin Lesion Classification Using Hybrid Deep Neural Networks 2017 [[paper]](https://arxiv.org/abs/1702.08434)
### Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble arXiv 2017 [[paper]](https://arxiv.org/abs/1703.03108)
### Knowledge Transfer for Melanoma Screening with Deep Learning 2017 [[paper]](https://ieeexplore.ieee.org/document/7950523/)

## 2018
### Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features IEEE TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8440053/)
### Classification for Dermoscopy Images Using Convolutional Neural Networks Based on Region Average Pooling IEEE Access 2018 [[paper]](https://ieeexplore.ieee.org/document/8502872)
### A Multi-task Framework with Feature Passing Module for Skin Lesion Classification and Segmentation IEEE ISBI 2018 [[paper]](https://ieeexplore.ieee.org/document/8363769/)
### Skin Lesion Analysis Toward Melanoma Detection IEEE ISBI 2018 [[paper]](https://ieeexplore.ieee.org/document/8363547/)
### A Deep Residual Architecture for Skin Lesion Segmentation ISIC 2018 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-01201-4_30)
### DermoNet Densely Linked Convolutional Neural Network for Efficient Skin Lesion Segmentation [[paper]](https://openreview.net/forum?id=B167qcojM)
### Techniques and Algorithms for Computer Aided Diagnosis of Pigmented Skin Lesions A Review [[paper]](https://www.sciencedirect.com/science/article/pii/S1746809417301428)
### MelanoGANs High Resolution Skin Lesion Synthesis with GANs [[paper]](https://arxiv.org/abs/1804.04338)
### SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks MICCAI 2018 [[paper]](https://arxiv.org/abs/1805.10241)
### Skin Lesion Classification with Ensemble of Squeeze-and-excitation Networks and Semi-supervised Learning 2018 [[paper]](https://arxiv.org/abs/1809.02568)

## 2019
### Deep Attention Model for the Hierarchical Diagnosis of Skin Lesions CVPRW 2019 [[paper]](http://openaccess.thecvf.com/content_CVPRW_2019/html/ISIC/Barata_Deep_Attention_Model_for_the_Hierarchical_Diagnosis_of_Skin_Lesions_CVPRW_2019_paper.html)
### DermaKNet Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis IEEE JBHI 2019 [[paper]](https://ieeexplore.ieee.org/document/8293766)
### Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features IEEE JBHI 2019 [[paper]](https://ieeexplore.ieee.org/document/8353143)
### Melanoma Recognition via Visual Attention IPMI 2019 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-20351-1_62)
### Skin Lesion Classification Using Convolutional Neural Network with Novel Regularizer IEEE Access 2019 [[paper]](https://ieeexplore.ieee.org/document/8669763)
### Solo or Ensemble Choosing a CNN Architecture for Melanoma Classification CVPRW 2019 [[paper]](http://openaccess.thecvf.com/content_CVPRW_2019/html/ISIC/Perez_Solo_or_Ensemble_Choosing_a_CNN_Architecture_for_Melanoma_Classification_CVPRW_2019_paper.html)
### Towards Automated Melanoma Detection with Deep Learning Data Purification and Augmentation CVPRW 2019 [[paper]](http://openaccess.thecvf.com/content_CVPRW_2019/html/ISIC/Bisla_Towards_Automated_Melanoma_Detection_With_Deep_Learning_Data_Purification_and_CVPRW_2019_paper.html)

## 2020
### Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model IEEE TMI 2020 [[paper]](https://arxiv.org/abs/2005.07377)
"The idea may be inspired by the paper titled 'Correlation Congruence for Knowledge Distillation ICCV 2019'. "
### A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification IEEE TMI 2020 [[paper]](https://arxiv.org/abs/1903.03313)

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# Endoscopy
## 2018
### Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks IEEE TMI 2018 [[paper]](https://ieeexplore.ieee.org/document/8259318/) [[code]](https://github.com/surgical-vision/EndoVisPoseAnnotation)
### 3-D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery IEEE TMI 2018 [[paper]](https://ieeexplore.ieee.org/document/8295119)
## 2019
### Quantification and Analysis of Laryngeal Closure From Endoscopic Videos IEEE TBME 2019 [[paper]](https://ieeexplore.ieee.org/document/8450618)
### Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking in surgical video MedIA 2019 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841519300593)
### Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video MICCAI 2019 [[paper]](https://link.springer.com/chapter/10.1007/978-3-030-32254-0_49)
### 2017 Robotic Instrument Segmentation Challenge arXiv 2019 [[paper]](https://arxiv.org/abs/1902.06426)
### Endoscopy artifact detection (EAD 2019) challenge dataset arXiv 2019 [[paper]](https://arxiv.org/abs/1905.03209)
### A deep learning framework for quality assessment and restoration in video endoscopy arXiv 2019 [[paper]](https://arxiv.org/abs/1904.07073)

## 2020
### Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video MICCAI 2020 [[paper]](https://arxiv.org/abs/2007.02501) [[code]](https://github.com/zxzhaoeric/Semi-InstruSeg)
### Multi-task recurrent convolutional network with correlation loss for surgical video analysis MedIA 2020 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841519301124)