A list of medical image datasets and challenges for deep learning¶
If the content on this page was helpful to you, or if one of our papers was helpful to you, please cite the following paper:
如果这个网页中的内容对你有帮助，或者我们的论文 对你有帮助，请引用如下论文：
Johann Li, Guangming Zhu, Cong Hua, Mingtao Feng, BasheerBennamoun, Ping Li, Xiaoyuan Lu, Juan Song, Peiyi Shen, Xu Xu, Lin Mei, Liang Zhang, Syed Afaq Ali Shah, and Mohammed Bennamoun. 2021. A Systematic Collection of Medical Image Datasets for Deep Learning. (June 2021). Retrieved from http://arxiv.org/abs/2106.12864
@article{Li2021,
archivePrefix = {arXiv},
arxivId = {2106.12864},
author = {Li, Johann and Zhu, Guangming and Hua, Cong and Feng, Mingtao and BasheerBennamoun and Li, Ping and Lu, Xiaoyuan and Song, Juan and Shen, Peiyi and Xu, Xu and Mei, Lin and Zhang, Liang and Shah, Syed Afaq Ali and Bennamoun, Mohammed},
eprint = {2106.12864},
month = {jun},
title = {{A Systematic Collection of Medical Image Datasets for Deep Learning}},
url = {http://arxiv.org/abs/2106.12864},
year = {2021}
}
Summary¶
Contents:
- Datasets and challenges summary of the head and neck
- Datasets and challenges summary of the basic brain image analysis.
- Datasets and challenges summary of the brain lesion and tumor segmentation task.
- Datasets and challenges summary of brain disease classification tasks.
- Datasets and challenges summary of eye-disease-concerned tasks.
- Datasets and challenges summary of other subjects in head and neck.
- Datasets summary of behavioral and perception concerning tasks.
- Datasets and challenges summary of the cheset and abdomen
- Datasets and challenges summary of the chest and abdomen organ segmentation tasks.
- Datasets and challenges summary of chest and abdomen organs-concerned tasks.
- Datasets and challenges summary of registration (R), estimation (E), localization (L), Reconstruction (RC), tracking (T), classification (C) and other tasks.
- Datasets and challenges summary of the pathology and blood
- Datasets and challenges summary of the others
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