深度学习医学图像分析文献集

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摘要

 

转自：爱可可-爱生活

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.

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).

List of Journals / Conferences (J/C):

• Medical Image Analysis (MedIA)

• IEEE Transaction on Medical Imaging (IEEE-TMI)

• IEEE Transaction on Biomedical Engineering (IEEE-TBME)

• 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)

Shortcuts

Deep Learning Techniques:

• 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

Imaging Modality:

• 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

Table of Contents

Deep Learning Techniques

• AutoEncoders/ Stacked AutoEncoders

• Convolutional Neural Networks

• Recurrent Neural Networks

• Generative Adversarial Networks

Medical Applications

• Annotation

• Classification

• Detection/ Localization

• Segmentation

• Registration

• Regression

• Other tasks

链接：

https://github.com/albarqouni/Deep-Learning-for-Medical-Applications

原文链接：

https://m.weibo.cn/1402400261/4162373902287270

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