[轉自github]基於深度學習的醫療影像論文匯總（Deep Learning Papers on Medical Image Analysis）

原文轉自github，作者：Shadi Albarqouni。

看到好東西，怎麼能不分享呢。

第一次在知乎翻譯，由於水平有限（不是謙虛的那種有限，是真的有限），有不準確的地方還望包涵，最重要的是，還望大佬們多多指正！

1，Background

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. 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.

這是第一個基於深度學習的醫療影像論文匯總。github上還有一些基於深度學習的計算機視覺論文匯總，比如Awesome Deep Vision；以及一些不限於應用的深度學習論文匯總，比如Awesome Deep Learning Papers。在這個匯總里，我會盡量根據不同的深度學習技術（deep learning techniques）和學習方法（learning methodology）去分類。

2，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).

1. 自2015年起，頂會頂刊上的深度學習論文;
2. 同行評議的期刊和知名度較高的會議，以及最近的arXiv（arXiv:CV & PR）論文。

醫療論文期刊/會議：

• Medical Image Analysis (MedIA)
• IEEE Transaction on Medical Imaging (IEEE-TMI)
• IEEE Transaction on Biomedical Engineering (IEEE-TBME)

PS：暑假師兄做的work投到了TBME，最近我接著師兄的work繼續做。我們的任務是Kaggle比賽的糖尿病視網膜病變檢測（Diabetic Retinopathy Detection ）。

• IEEE Journal of Biomedical and Health Informatics (IEEE-JBHI)
• International Journal on Computer Assisted Radiology and Surgery (IJCARS)
• International Conference on Information Processing in Medical Imaging (IPMI)
• International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
• International Conference on Information Processing in Computer-Assisted Interventions (IPCAI)
• IEEE International Symposium on Biomedical Imaging (ISBI)

3，Shortcuts

3.1，深度學習技術：

• NN: Neural Networks
• MLP: Multilayer Perceptron
• RBM: Restricted Boltzmann Machine
• SAE: Stacked Auto-Encoders
• CAE: Convolutional Auto-Encoders
• CNN: Convolutional Neural Networks
• RNN: Recurrent Neural Networks
• LSTM: Long Short Term Memory
• M-CNN: Multi-Scale/View/Stream CNN
• FCN: Fully Convolutional Networks

3.2，成像方式：

• US: Ultrasound
• MR/MRI: Magnetic Resonance Imaging
• PET: Positron Emission Tomography
• MG: Mammography
• CT: Computed Tompgraphy
• H&E: Hematoxylin & Eosin Histology Images
• RGB: Optical Images

4，Table of Contents

4.1，Deep Learning Techniques

• AutoEncoders/ Stacked AutoEncoders
• Convolutional Neural Networks
• Recurrent Neural Networks
• Generative Adversarial Networks

4.2，Medical Applications

• Annotation
• Classification
• Detection/ Localization
• Segmentation
• Registration
• Regression
• Other tasks

5，Deep Learning Techniques

5.1，Auto-Encoders/ Stacked Auto-Encoders

5.2，Convolutional Neural Networks

• AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
• Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

5.3，Recurrent Neural Networks

5.4，Generative Adversarial Networks

6，Medical Applications

Annotation

1. Deep learning of feature representation with multiple instance learning for medical image analysis [pdf]
2. AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images [pdf]

Classification

1. Multi-scale Convolutional Neural Networks for Lung Nodule Classification [pdf]
2. Predicting Alzheimers disease: a neuroimaging study with 3D convolutional neural networks [pdf]
3. Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning [pdf]
4. Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks [pdf]
5. A Deep Semantic Mobile Application for Thyroid Cytopathology [pdf]
6. Alzheimers Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network [pdf]
7. Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers [pdf]
8. Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes [pdf]
9. Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks [pdf]
10. 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients [pdf]
11. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans [pdf]
12. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring [pdf]
13. Spectral Graph Convolutions for Population-based Disease Prediction [pdf]
14. SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks [pdf]

Detection / Localization

1. 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data [pdf]
2. Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks [pdf]
3. Automated anatomical landmark detection ondistal femur surface using convolutional neural network [pdf]
4. Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks [pdf]
5. Regressing Heatmaps for Multiple Landmark Localization using CNNs [pdf]
6. An artificial agent for anatomical landmark detection in medical images [pdf]
7. Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound using Fully Convolutional Neural Networks [pdf]
8. Recognizing end-diastole and end-systole frames via deep temporal regression network [pdf]
9. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation Neural Networks [pdf]
10. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique Neural Networks [pdf]
11. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks [pdf]
12. Self-Transfer Learning for Fully Weakly Supervised Lesion Localization [pdf]
13. Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images [pdf]

Segmentation

1. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation [pdf]
2. Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields [pdf]
3. Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks [pdf]
4. SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks [pdf]
5. q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI [pdf] (Section II.B.2)

Registration

1. An Artificial Agent for Robust Image Registration [pdf]

Regression

1. Automated anatomical landmark detection ondistal femur surface using convolutional neural network [pdf]
2. q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI [pdf] (Section II.B.1)

原文轉自github，作者：Shadi Albarqouni。

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