To the best of our knowledge, this is the first list of deep learning papers on medical applications. There are couple of lists for deep learning papers in general, or computer vision, for example Awesome Deep Learning Papers(http://t.cn/R5GgRIi). In this list, I try to classify the papers based on their deep learning techniques and learning methodology. I believe this list could be a good starting point for DL researchers on Medical Applications.
这是第一个基于深度学习的医疗影像论文汇总。github上还有一些基于深度学习的计算机视觉论文汇总,比如Awesome Deep Vision(http://t.cn/RLvTzjn);以及一些不限于应用的深度学习论文汇总,比如Awesome Deep Learning Papers(http://t.cn/R5GgRIi)。在这个汇总里,我会尽量根据不同的深度学习技术(deep learning techniques)和学习方法(learning methodology)去分类。
Criteria
1,A list of top deep learning papers published since 2015.
2,Papers are collected from peer-reviewed journals and high reputed conferences. However, it may have recent papers on arXiv.
3,A meta-data is required along with the paper, i.e. Deep Learning technique, Imaging Modality, Area of Interest, Clinical Database (DB).
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images(http://t.cn/RWA1lmT)
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images(http://t.cn/RWA1Rma)
5.3,Recurrent Neural Networks
5.4,Generative Adversarial Networks
Medical Applications
Annotation
Deep learning of feature representation with multiple instance learning for medical image analysis(http://t.cn/RWA1FkV)
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images (http://t.cn/RWABUT7)
Classification
Multi-scale Convolutional Neural Networks for Lung Nodule Classification(http://t.cn/RWADf0A)
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks (http://t.cn/RWADSK4)
Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning (http://t.cn/RWADYxw)
Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks (http://t.cn/RWADk5G)
A Deep Semantic Mobile Application for Thyroid Cytopathology (http://t.cn/RWAko5r)
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network (http://t.cn/RWAkcoj)
Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers (http://t.cn/RWAkWVF)
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes (http://t.cn/RWAkEnF)
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks (http://t.cn/RWAF7qb)
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients (http://t.cn/RWAkkPX)
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (http://t.cn/RWAFyHc)
Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring (http://t.cn/RWAFILN)
Spectral Graph Convolutions for Population-based Disease Prediction (http://t.cn/RWAFohq)
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks (http://t.cn/RWAFYuV)
Detection / Localization
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data (http://t.cn/RWAstTB)
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks (http://t.cn/RWAs6xr)
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks (http://t.cn/RWAsn1T)
Regressing Heatmaps for Multiple Landmark Localization using CNNs (http://t.cn/RW2vv2L)
An artificial agent for anatomical landmark detection in medical images (http://t.cn/RW2vy2P)
Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound using Fully Convolutional Neural Networks (http://t.cn/RW2vft1)
Recognizing end-diastole and end-systole frames via deep temporal regression network (http://t.cn/RW2vrQW)
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation Neural Networks (http://t.cn/RW2vrQW)
Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique Neural Networks (http://t.cn/RW2hTcw)
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks (http://t.cn/RW2Pu8C)
Self-Transfer Learning for Fully Weakly Supervised Lesion Localization (http://t.cn/RW27xd4)
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images (http://t.cn/RWA1Rma)
Segmentation
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (http://t.cn/RW27lTz)
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields (http://t.cn/RW27n2Y)
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks (http://t.cn/RibGTxx)
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks (http://t.cn/RWAFYuV)
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI (http://t.cn/RW2zfRN)(Section II.B.2)
Registration
An Artificial Agent for Robust Image Registration (http://t.cn/RW2zWw4)