Deep Learning in Medical Imaging and Medical Image Analysis
Review and Survey
[paper]
Guest Editorial Deep Learning in Medical Imaging Overview and Future Promise of an Exciting New Technique 2016[paper]
Overview of Deep Learning in Medical Imaging 2017[paper]
A Survey on Deep Learning in Medical Image Analysis 2017[paper]
Deep Learning Applications in Medical Image Analysis 2017[paper]
Deep Learning in Medical Image Analysis 2017[paper]
Deep Learning in Microscopy Image Analysis A Survey 2017[paper]
GANs for Medical Image Analysis arXiv 2018[paper]
Generative Adversarial Network in Medical Imaging: A Review arXiv 2018[paper]
Deep Learning in Medical Image Registration: A Survey arXiv 2019[paper]
Deep Learning in Medical Image Registration: A Review arXiv 2019[paper]
Deep Learning in Medical Ultrasound Analysis A Review Engineering 2019[paper]
Deep Learning in Cardiology arXiv 2019[paper]
Deep learning in Medical Imaging and Radiation Therapy MP 2019[paper]
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges JDI 2019[arXiv paper] [MedIA paper]
Embracing Imperfect Datasets A Review of Deep Learning Solutions for Medical Image Segmentation MedIA 2020[paper]
Machine Learning Techniques for Biomedical Image Segmentation An Overview of Technical Aspects and Introduction to State-of-Art Applications arXiv 2019[paper]
Deep Neural Network Models for Computational Histopathology A Survey arXiv 2019[paper]
A Survey on Domain Knowledge Powered Deep Learning for Medical Image Analysis arXiv 2020[paper]
State-of-the-Art Deep Learning in Cardiovascular Image Analysis JACC 2019[paper]
A Review of Deep Learning in Medical Imaging Image Traits Technology Trends Case Studies with Progress Highlights and Future Promises arXiv 2020[paper]
Review of Artificial Intelligence Techniques in Imaging Data Acquisition Segmentation and Diagnosis for COVID-19 IEEE RBME 2020[paper] [arXiv paper]
Model-Based and Data-Driven Strategies in Medical Image Computing IEEE Proceedings 2020[paper]
Deep Learning Based Brain Tumor Segmentation A Survey arXiv 2020[paper]
A Review Deep Learning for Medical Image Segmentation Using Multi-modality Fusion arXiv 2020[paper]
Medical Instrument Detection in Ultrasound-Guided Interventions A Review arXiv 2020[paper]
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]
Learning-based Algorithms for Vessel Tracking A Review arXiv 2020[paper]
Deep Learning for Cardiac Image Segmentation A Review FCVM 2020[paper]
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology Circulation 2020[paper]
Overview of the Whole Heart and Heart Chamber Segmentation Methods CET 2020[paper]
Deep Learning for Chest X-ray Analysis A Survey arXiv 2021[paper]
Multi-Modality Cardiac Image Computing A Survey arXiv 2022[paper]
Nuclei & Glands Instance Segmentation in Histology Images A Narrative Review arXiv 2022Datasets
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)"
[dataset]
ChestX-ray8 Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases CVPR 2017"Chest Radiographs"
[dataset]
KiTS 2019"300 Abdomen CT scans for kidney and tumor segmentation"
[dataset]
CHD_Segmentation"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
[paper]
ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection arXiv 2017"ISIC2016", "ISIC2017", "ISIC2018", "ISIC2019"
[paper]
VerSe A Vertebrae Labelling and Segmentation Benchmark arXiv 2020"VerSe"
[paper]
A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology IEEE TMI 2017[paper]
A Multi-Organ Nucleus Segmentation Challenge IEEE TMI 2020"MoNuSeg"
[paper]
Deep Learning to Segment Pelvic Bones Large-scale CT Datasets and Baseline Models arXiv 2020"CTPelvic1K"
[paper]
RibSeg v2 A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction arXiv 2022"RibSeg"
Computed Tomography (CT)
2022
[paper] [code]
Learning Topological Interactions for Multi-Class Medical Image Segmentation ECCV Oral 20222015
[paper]
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data MICCAI 20152016
[paper]
An Artificial Agent for Anatomical Landmark Detection in Medical Images MICCAI 2016"deep reinforcement learning", "anatomical landmark detection"
[paper]
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields MICCAI 2016"CRF"
[paper]
Low-dose CT Denoising with Convolutional Neural Network[paper]
Low-Dose CT via Deep Neural Network[paper]
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks[paper]
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation IEEE TMI 20162017
[paper]
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss[paper]
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network MICCAI 2017[paper]
Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network[paper]
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT[paepr]
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image[paper]
A Self-aware Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation[paper]
DeepLesion Automated Deep Mining Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations[paper]
Unsupervised End-to-end Learning for Deformable Medical Image Registration[paper]
DeepLung 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification[paper]
CT Image Denoising with Perceptive Deep Neural Networks[paper]
Improving Low-Dose CT Image Using Residual Convolutional Network[paper]
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)[paper]
Stacked Competitive Networks for Noise Reduction in Low-dose CT[paper]
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network[paper]
Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning[paper]
Robust Multi-scale Anatomical Landmark Detection in Incomplete 3D-CT Data[paper]
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans TPAMI 2017[paper]
3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images MedIA 2017"deep supervision mechanism"
[paper]
Generative Adversarial Networks for Noise Reduction in Low-Dose CT IEEE TMI 20172018
[paper]
A Two-stage 3D Unet Framework for Multi-class Segmentation on Full Resolution Image arXiv 2018[paper]
DeepLung Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification[paper]
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans[paper]
Attention U-Net Learning Where to Look for the Pancreas[paper]
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network[paper]
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network[paper]
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising[paper]
Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data MedIA 2018[paper]
Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images Arxiv 2018"reinforcement learning", "anatomical landmark localization", "aortic valve". "left atrial appendage"
[paper]
Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation[paper]
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network CVPR 2018[paper]
AnatomyNet Deep 3D Squeeze-and-excitation U-Nets for Fast and Fully Automated Whole-volume Anatomical Segmentation Medical Physics 2018[paper]
DeepEM Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection MICCAI 2018[paper]
Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks 2018[paper]
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior[paper]
Deep Learning Based Rib Centerline Extraction and Labeling[paper]
Liver Lesion Detection from Weakly-Labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector MICCAI 2018[paper]
CFUN Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation 2018[paper]
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]
3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas CR 2018[paper]
(AH-Net) 3D Anisotropic Hybrid Network Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes MICCAI 2018"liver and liver tumor segmentation from a Computed Tomography volume", "lesion detection from a Digital Breast Tomosynthesis volume"
[paper]
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]
Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs IJCARS 20182019
[paper]
3DFPN-HS2 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection MICCAI 2019[paper]
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography IEEE TMI 2019[paper]
Abdominal Multi-organ Segmentation with Organ-attention Networks and Statistical Fusion MedIA 2019[paper]
Attention Gated Networks Learning to Leverage Salient Regions in Medical Images MedIA 2019[paper] [CMIG paper]
Automated Coronary Artery Atherosclerosis Detection and Weakly Supervised Localization on Coronary CT Angiography with a Deep 3-Dimensional Convolutional Neural Network arXiv 2019[paper]
Automated Design of Deep Learning Methods for Biomedical Image Segmentation arXiv 2019[paper]
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] [arXiv paper]
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-based Orientation Classifier MedIA 2019[paper]
Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics MICCAI 2019[paper]
Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans arXiv 2019[paper]
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT IEEE TMI 2019[paper]
Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography MICCAI 2019[paper]
Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network IEEE TMI 2019[paper]
Motion Artifact Recognition and Quantification in Coronary CT Angiography Using Convolutional Neural Networks MedIA 2019[paper]
Motion Estimation and Correction in Cardiac CT Angiography Images Using Convolutional Neural Networks CMIG 20192020
[paper]
3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT MICCAI 2020[paper]
Bounding Maps for Universal Lesion Detection arXiv 2020[paper]
C2FNAS Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation CVPR 2020[paper] [code]
Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation MICCAI 2020[paper]
CPR-GCN Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries CVPR 2020[paper]
Deep Distance Transform for Tubular Structure Segmentation in CT Scans CVPR 2020""
[paper]
Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography IEEE TMI 2020[paper]
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images arXiv 2020[paper]
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images arXiv 2020"textures and edge information"
[paper]
Going to Extremes Weakly Supervised Medical Image Segmentation arXiv 2020[paper]
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]
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"improvement of CNN-based Orientation Classifier (vessel tracker)"
[paper]
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]
PGL Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation arXiv 2020RA-UNet A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans 2020
[paper]
Rapid Vessel Segmentation and Reconstruction of Head and Neck Angiograms Using 3D Convolutional Neural Network NC 2020[paper]
SenseCare A Research Platform for Medical Image Informatics and Interactive 3D Visualization arXiv 2020[paper]
TopNet Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling MICCAI 2020[paper]
TripletUNet Multi-Task U-Net with Online Voxel-Wise Learning for Precise CT Prostate Segmentation arXiv 2020[paper]
UXNet Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation arXiv 20202021
[paper]
Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss arXiv 2021[paper]
CoTr Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation arXiv 2021[paper]
Swin-Unet Unet-like Pure Transformer for Medical Image Segmentation arXiv 2021[paper]
Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement arXiv 20212022
[paper]
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 2022Magnetic Resonance Imaging (MRI)
2022
[paper]
(RefSeg) Online Reflective Learning for Robust Medical Image Segmentation MICCAI 20222015
[paper]
Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation MICCAI 20152016
[paper]
Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation[paper]
Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks MICCAI 2016"CRF"
[paper]
Regressing Heatmaps for Multiple Landmark Localization Using CNNs MICCAI 2016"Multiple Landmark Localization"
2017
[paper]
SegAN Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation[paper]
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images[paper]
Deep MR to CT Synthesis using Unpaired Data[paper]
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT[paper] [code]
3D Fully Convolutional Networks for Subcortical Segmentation in MRI A Large-scale Study[paper]
2D-3D Fully Convolutional Neural Networks for Cardiac MR SegmentationAutomatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
[paper]
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI[paper]
Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation[paper]
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks[paper]
Deep Learning with Domain Adaptation for Accelerated Projection Reconstruction MR[paper]
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction[paper]
Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks[paper]
Learning a Variational Network for Reconstruction of Accelerated MRI Data[paper]
A Parallel MR Imaging Method Using Multilayer Perceptron[paper]
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction[paper]
Image Reconstruction by Domain Transform Manifold Learning[paper]
Human-level CMR Image Analysis with Deep Fully Convolutional Networks[paper]
A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue MICCAI 2017"CRF"
[paper]
Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation MICCAI 2017"CRF"
[paper] [arXiv paper]
Medical Image Synthesis with Context-aware Generative Adversarial Networks MICCAI 20172018
[paper]
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks[paper]
3D Multi-scale FCN with Random Modality Voxel Dropout Learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images[paper]
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network[paper]
Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks[paper]
k-Space Deep Learning for Accelerated MRI[paper]
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation[paper]
Deformable Image Registration Using a Cue-Aware Deep Regression Network TBME 2018[paper]
Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images TBME 2018[paper]
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes MICCAI 2018"focal loss", "Exponential Logarithmic Loss"
[paper]
Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks 2018[paper]
An Unsupervised Learning Model for Deformable Medical Image Registration CVPR 2018[paper]
VoxelMorph: A Learning Framework for Deformable Medical Image Registration IEEE TMI 2018[paper]
Direct Delineation of Myocardial Infarction without Contrast Agents Using a Joint Motion Feature Learning Architecture MedIA 2018[paper]
Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation IEEE TMI 2018[paper]
Towards MR-Only Radiotherapy Treatment Planning: Synthetic CT Generation Using Multi-view Deep Convolutional Neural Networks MICCAI 2018[paper] [arXiv paper]
Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN DLMIA 20182019
[paper] [code]
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation MICCAI 2019[paper]
Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network IEEE TMI 20192020
[paper]
Automated Intracranial Artery Labeling Using a Graph Neural Network and Hierarchical Refinement MICCAI 2020[paper]
Brain Tumor Segmentation Using 3D-CNNs with Uncertainty Estimation arXiv 2020[paper]
CA-Net Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation arXiv 2020[paper]
(CANet) CANet Context Aware Network for 3D Brain Tumor Segmentation arXiv 2020[paper]
Cardiac Segmentation with Strong Anatomical Guarantees arXiv 2020[paper]
CS2-Net Deep Learning Segmentation of Curvilinear Structures in Medical Imaging arXiv 2020[paper] [paper]
Deep Morphological Simplification Network MS-Net for Guided Registration of Brain Magnetic Resonance Images PR 2019[paper]
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network arXiv 2020[paper]
Knowledge Distillation for Brain Tumor Segmentation arXiv 2020[paper]
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"AsynDGAN is comprised of one central generator and multiple distributed discriminators located in different medical entities."
[paper]
Two-Stage Cascaded U-Net 1st Place Solution to BraTS Challenge 2019 Segmentation Task BrainLes 2019[paper]
UNet++ Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation IEEE TMI 2020[paper]
Ο-Net Stacking Densely Convolutional LSTMs for Sub-cortical Brain Structure Segmentation IEEE TMI 20202021
[paper] [PyTorch code]
TransBTS Multimodal Brain Tumor Segmentation Using Transformer arXiv 20212022
[paper]
Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis arXiv 2022Ultrasound (US)
2015
[paper]
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks MICCAI 2015[paper]
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks IEEE JBHI 20152016
[paper]
Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset[paper]
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]
Describing Ultrasound Video Content Using Deep Convolutional Neural Networks 20162017
[paepr]
Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning[paper]
Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks[paper]
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning[paper]
Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation[paper]
Hough-CNN Deep learning for segmentation of deep brain regions in MRI and ultrasound CVIU 2017[paper]
Cascaded Fully Convolutional Networks for Automatic Prenatal Ultrasound Image Segmentation 2017[paper]
Ultrasound Standard Plane Detection Using a Composite Neural Network Framework 2017[paper]
CNN-based Estimation of Abdominal Circumference from Ultrasound Images 2017Ultrasound Image-based Thyroid Nodule Automatic Segmentation Using Convolutional Neural Networks IJCARS 2017 [[paper]]
"thyroid"
[paper] [arXiv paper]
SonoNet Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound IEEE TMI 20172018
[paper]
A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification IEEE TBME 2018[paper]
Adversarial Image Registration with Application for MR and TRUS Image Fusion 2018[paper]
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection 2018[paper]
Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting[paepr]
Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound[paper]
Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging 2018[paper] [TF code]
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network MICCAI 2018[paper]
Fully-automated Alignment of 3D Fetal Brain Ultrasound to a Canonical Reference Space Using Multi-task Learning MedIA 2018[paper]
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model 2018[paper]
High Frame-rate Cardiac Ultrasound Imaging with Deep Learning MICCAI 2018[paper]
High Quality Ultrasonic Multi-line Transmission through Deep Learning MICCAI 2018[paper]
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks[paper]
Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks CR 2018[paper]
Less is More Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images 2018[paper]
Multi-task SonoEyeNet Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps MICCAI 2018[paper]
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network 2018[paper]
Weakly Supervised Localisation for Fetal Ultrasound Images DLMIAW 20182019
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)
[paper]
Automated Detection and Classification of Thyroid Nodules in Ultrasound Images Using Clinical-knowledge-guided Convolutional Neural Networks MedIA 2019"thyroid"
2020
[paper]
Contrastive Rendering for Ultrasound Image Segmentation arXiv 2020[paper]
Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network IEEE TBME 2020[paper]
Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging IEEE TMI 2020[paper] [code]
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images MICCAI 2020[paper] [code]
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis IEEE TMI 2020X-ray
2015
[paper]
Deep Learning and Structured Prediction for the Segmentation of Mass in Mamograms MICCAI 20152016
[paper]
Learning to Read Chest X-Rays Recurrent Neural Cascade Model for Automated Image Annotation 20162017
[paper]
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks DLMIA 2017[paper]
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks[paper]
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks 2017"reimplement this recently", "segmentation data for normalization was done"
[paper]
Cascade of Multi-scale Convolutional Neural Networks for Bone Suppression of Chest Radiographs in Gradient Domain 2017[paper]
CheXNet Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning 2017[paper]
Adversarial Deep Structural Networks for Mammographic Mass Segmentation MICCAI 2017[paper]
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification MICCAI 2017[paper]
A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification 2017[paper]
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks 2017[paper]
Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning TMI 2017Deep Learning for Automated Skeletal Bone Age Assessment in X-ray Images MedIA 2017
"focus on this recently (20181001)"
2018
[paper]
SCAN Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays[TMI paper] [ArXiv paper]
Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs IEEE TMI 2018[paper]
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation 2018[paper]
LF-SegNet A Fully Convolutional EncoderβDecoder Network for Segmenting Lung Fields from Chest Radiographs 2018[paper]
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]
Breast Mass Segmentation and Shape Classification in Mammograms Using Deep Neural Networks"conditional generative adversarial networks", "INbreast", "digital database for screening mammography (DDSM)"
[paper]
Medical Image Description Using Multi-task-loss CNN 2016[paper]
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification MICCAI 2018[paper]
Benign and malignant breast tumors classification based on region growing and CNN segmentation ESA 2015[paper]
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms ISBI 2018[paper]
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net MICCAI 2018[paper]
Thoracic Disease Identification and Localization with Limited Supervision CVPR 2018[paper]
Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions 2018[paper]
Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning CBM 2018[paper]
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder RAMBO 20182019
[paper]
Accurate Automated Cobb Angles Estimation Using Multi-view Extrapolation Net MedIA 2019[paper]
Learning to Detect Chest Radiographs Containing Pulmonary Lesions Using Visual Attention Networks MedIA 2019[paper]
When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification IEEE ISBI 20192020
[paper]
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 20202021
[paper]
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 2021Positron Emission Tomography (PET)
2017
[paper]
Combo Loss Handling Input and Output Imbalance in Multi-Organ Segmentation arXiv 2018[paper]
Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results arXiv 20172018
[paper]
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation IEEE TMI 2018[paper]
PET Image Reconstruction Using Deep Image Prior IEEE TMI 20182019
[paper]
Cross-modality Synthesis from CT to PET Using FCN and GAN Networks for Improved Automated Lesion Detection ENGAPPAI 2019Funduscopy
2016
[paper]
DeepVessel Retinal Vessel Segmentation via Deep Learning and Conditional Random Field MICCAI 2016"CRF"
2017
[paper] [Keras+TF code]
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks[paper] [code]
Towards Adversarial Retinal Image Synthesis arXiv 20172018
[paper] [code]
End-to-End Adversarial Retinal Image Synthesis IEEE TMI 2018[paper]
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation TMI 2018[paper]
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation TBME 20182019
[paper]
CE-Net: Context Encoder Network for 2D Medical Image Segmentation IEEE TMI 2019[paper] [TF code]
Deep Vessel Segmentation by Learning Graphical Connectivity MedIA 20192020
[paper]
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation arXiv 2020"IVUS images are similar to Eye Fundus Images."
Microscopy
2016
[paper]
Stain Normalization Using Sparse AutoEncoders (StaNoSA) Application to Digital Pathology[paper]
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images IEEE TMI 20162017
[paper]
Adversarial Image Alignment and Interpolation[paper]
CNN Cascades for Segmenting Whole Slide Images of the Kidney[paper]
Learning to Segment Breast Biopsy Whole Slide Images[paper]
SFCN-OPI Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction[paper]
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network CVPR 20172018
[paper]
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification ICIAR 2018[paper]
Cancer Metastasis Detection With Neural Conditional Random Field MIDL 2018[paper]
DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks MedIA 20182019
[paper] [arXiv paper]
Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation MICCAI 2019[paper]
HoVer-Net Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images MedIA 2019[paper]
Weakly supervised mitosis detection in breast histopathology images using concentric loss MedIA 20192020
[paper]
Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation MICCAI 2020[paper]
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 20202022
[paper]
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 2022Colonoscopy
2016
[papr]
Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning TMI 20162018
[paper]
Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional NetworkOCT
2017
[paper]
Cystoid Macular Edema Segmentation of Optical Coherence Tomography Images Using Fully Convolutional Neural Networks and Fully Connected CRFs 2017Dermoscopy
2016
[paepr]
Automatic Melanoma Detection via Multi-scale Lesion-biased Representation and Joint Reverse Classification IEEE ISBI 2016[paper]
Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector[paper]
Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification2017
[paper]
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks IEEE TMI 2017[paper]
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance"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."
[paper]
Investigating deep side layers for skin lesion segmentation[paper]
Skin Lesion Segmentation via Deep RefineNet[paper]
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks[paper]
Segmentation of dermoscopy images based on fully convolutional neural network[paper]
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks"Multi-class (classification and segmentation)"
[paper]
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks[paper]
Dermoscopic Image Segmentation via Multi-Stage Fully Convolutional Networks[paper]
Skin Melanoma Segmentation Using Recurrent and Convolutional Neural Networks IEEE ISBI 2017[paper]
Skin Lesion Classification Using Hybrid Deep Neural Networks 2017[paper]
Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble arXiv 2017[paper]
Knowledge Transfer for Melanoma Screening with Deep Learning 20172018
[paper]
Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features IEEE TBME 2018[paper]
Classification for Dermoscopy Images Using Convolutional Neural Networks Based on Region Average Pooling IEEE Access 2018[paper]
A Multi-task Framework with Feature Passing Module for Skin Lesion Classification and Segmentation IEEE ISBI 2018[paper]
Skin Lesion Analysis Toward Melanoma Detection IEEE ISBI 2018[paper]
A Deep Residual Architecture for Skin Lesion Segmentation ISIC 2018[paper]
DermoNet Densely Linked Convolutional Neural Network for Efficient Skin Lesion Segmentation[paper]
Techniques and Algorithms for Computer Aided Diagnosis of Pigmented Skin Lesions A Review[paper]
MelanoGANs High Resolution Skin Lesion Synthesis with GANs[paper]
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks MICCAI 2018[paper]
Skin Lesion Classification with Ensemble of Squeeze-and-excitation Networks and Semi-supervised Learning 20182019
[paper]
Deep Attention Model for the Hierarchical Diagnosis of Skin Lesions CVPRW 2019[paper]
DermaKNet Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis IEEE JBHI 2019[paper]
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features IEEE JBHI 2019[paper]
Melanoma Recognition via Visual Attention IPMI 2019[paper]
Skin Lesion Classification Using Convolutional Neural Network with Novel Regularizer IEEE Access 2019[paper]
Solo or Ensemble Choosing a CNN Architecture for Melanoma Classification CVPRW 2019[paper]
Towards Automated Melanoma Detection with Deep Learning Data Purification and Augmentation CVPRW 20192020
[paper]
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model IEEE TMI 2020"The idea may be inspired by the paper titled 'Correlation Congruence for Knowledge Distillation ICCV 2019'. "