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]
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 2018 [paper]
Generative Adversarial Network in Medical Imaging: A Review 2018 [paper]
Deep Learning in Medical Image Registration: A Survey 2019 [paper]
Deep Learning in Medical Image Registration: A Review 2019 [paper]
Deep Learning in Medical Ultrasound Analysis A Review Engineering 2019 [paper]
Deep Learning in Cardiology 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 [paper]
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation arXiv [paper]
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications arXiv [paper]
Deep Neural Network Models for Computational Histopathology: A Survey 2019 [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]
"Chest Radiographs"
KiTS 2019 [dataset]
"300 Abdomen CT scans for kidney and tumor segmentation"
CHD_Segmentation [dataset]
"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]
"ISIC2016", "ISIC2017", "ISIC2018", "ISIC2019"
Computed Tomography (CT)
2015
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data MICCAI 2015 [paper]
2016
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]
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields MICCAI 2016 [paper]
"CRF"
An Artificial Agent for Anatomical Landmark Detection in Medical Images MICCAI 2016 [paper]
"deep reinforcement learning", "anatomical landmark detection"
2017
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 [paper]
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image [paepr]
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 [paper]
"deep supervision mechanism"
2018
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]
Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network [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 [paper]
"reinforcement learning", "anatomical landmark localization", "aortic valve". "left atrial appendage"
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 [paper]
"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]
2019
Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network IEEE TMI 2019 [paper]
3DFPN-HS2 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection MICCAI 2019 [paper]
Abdominal Multi-organ Segmentation with Organ-attention Networks and Statistical Fusion MedIA 2019 [paper]
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography IEEE TMI 2019 [paper]
Attention Gated Networks Learning to Leverage Salient Regions in Medical Images MedIA 2019 [paper]
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-based Orientation Classifier MedIA 2019 [paper]
2020
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images arXiv 2020 [paper]
"textures and edge information"
Magnetic Resonance Imaging (MRI)
2016
Medical Image Synthesis with Context-aware Generative Adversarial Networks [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 [paper]
"CRF"
Regressing Heatmaps for Multiple Landmark Localization Using CNNs MICCAI 2016 [paper]
"Multiple Landmark Localization"
2017
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]
3D Fully Convolutional Networks for Subcortical Segmentation in MRI A Large-scale Study [paper] [code]
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation [paper]
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
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 [paper]
"CRF"
Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation MICCAI 2017 [paper]
"CRF"
2018
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 [paper]
"focal loss", "Exponential Logarithmic Loss"
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]
2019
Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network IEEE TMI 2019 [paper]
2020
Knowledge Distillation for Brain Tumor Segmentation arXiv 2020 [paper]
Ultrasound (US)
2015
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks [paper]
2016
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 2016 [paper]
2017
Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning [paepr]
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 2017 [paper]
2018
Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting [paper]
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks [paper]
Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound [paepr]
Adversarial Image Registration with Application for MR and TRUS Image Fusion 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]
Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging 2018 [paper]
Weakly Supervised Localisation for Fetal Ultrasound Images DLMIAW 2018 [paper]
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network 2018 [paper]
Less is More Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images 2018 [paper]
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection 2018 [paper]
A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification IEEE TBME 2018 [paper]
Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks CR 2018 [paper]
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)
X-ray
2015
Deep Learning and Structured Prediction for the Segmentation of Mass in Mamograms MICCAI 2015 [paper]
2016
Learning to Read Chest X-Rays Recurrent Neural Cascade Model for Automated Image Annotation 2016 [paper]
2017
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 [paper]
"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]
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 2017 [paper]
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]
Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs IEEE TMI 2018 [TMI paper] [ArXiv paper]
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation 2018 [paper]
LF-SegNet A Fully Convolutional Encoder–Decoder 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 [paper]
"conditional generative adversarial networks", "INbreast", "digital database for screening mammography (DDSM)"
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 2018 [paper]
2019
Learning to detect chest radiographs containing pulmonary lesions using visual attention networks MedIA 2019 [paper]
Accurate Automated Cobb Angles Estimation Using Multi-view Extrapolation Net MedIA 2019 [paper]
2020
Vertebra-Focused Landmark Detection for Scoliosis Assessment ISBI 2020 [paper]
Positron Emission Tomography (PET)
2017
Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results [paper]
Combo Loss Handling Input and Output Imbalance in Multi-Organ Segmentation Arxiv 2018 [paper]
2018
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation IEEE TMI 2018 [paper]
PET Image Reconstruction Using Deep Image Prior IEEE TMI 2018 [paper]
Funduscopy
2016
DeepVessel Retinal Vessel Segmentation via Deep Learning and Conditional Random Field MICCAI 2016 [paper]
"CRF"
2017
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [paper] [Keras+TF code]
2018
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation TBME 2018 [paper]
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation TMI 2018 [paper]
2019
CE-Net: Context Encoder Network for 2D Medical Image Segmentation IEEE TMI 2019 [paper]
Microscopy
2016
Stain Normalization Using Sparse AutoEncoders (StaNoSA) Application to Digital Pathology [paper]
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images [paper]
2017
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 2017 [paper]
2018
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 2018 [paper]
2019
Weakly supervised mitosis detection in breast histopathology images using concentric loss MedIA 2019 [paper]
Colonoscopy
2016
Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning TMI 2016 [papr]
2018
Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional Network [paper]
OCT
2017
Cystoid Macular Edema Segmentation of Optical Coherence Tomography Images Using Fully Convolutional Neural Networks and Fully Connected CRFs 2017 [paper]
Dermoscopy
2016
Automatic Melanoma Detection via Multi-scale Lesion-biased Representation and Joint Reverse Classification IEEE ISBI 2016 [paepr]
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 classification [paper]
2017
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 [paper]
"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]
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 [paper]
"Multi-class (classification and segmentation)"