转自:爱可可-爱生活
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.
A list of top deep learning papers published since 2015.
Papers are collected from peer-reviewed journals and high reputed conferences. However, it may have recent papers on arXiv.
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)
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
AutoEncoders/ Stacked AutoEncoders
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
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
虽然像CNNs这样的深度学习模型在医学图像分析方面取得了很大的成功,但是小型的医学数据集仍然是这一领域的主要瓶颈。为了解决这个问题,研究人员开始寻找现有医疗数据集之外的外部信息。传统的方法通常利用来自自然图像的信息。最近的研究利用了来自医生的领域知识,通过让网络模仿他们如何被训练,模仿他们的诊断模式,或者专注于他们特别关注的特征或领域。本文综述了将医学领域知识引入疾病诊断、病变、器官及异常检测、病变及器官分割等深度学习模型的研究进展。针对不同类型的任务,我们系统地对所使用的不同类型的医学领域知识进行了分类,并给出了相应的整合方法。最后,我们总结了挑战、未解决的问题和未来研究的方向。
With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.