The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require many resources, such as human expertise and funding. That makes it difficult for non-medical researchers to have access to useful and large medical data. Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected information of around three hundred datasets and challenges mainly reported between 2013 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood, and ``others''. Our paper has three purposes: 1) to provide a most up to date and complete list that can be used as a universal reference to easily find the datasets for clinical image analysis, 2) to guide researchers on the methodology to test and evaluate their methods' performance and robustness on relevant datasets, 3) to provide a ``route'' to relevant algorithms for the relevant medical topics, and challenge leaderboards.
@article{arxiv.2106.12864,
title = {A Systematic Collection of Medical Image Datasets for Deep Learning},
author = {Johann Li and Guangming Zhu and Cong Hua and Mingtao Feng and BasheerBennamoun and Ping Li and Xiaoyuan Lu and Juan Song and Peiyi Shen and Xu Xu and Lin Mei and Liang Zhang and Syed Afaq Ali Shah and Mohammed Bennamoun},
journal= {arXiv preprint arXiv:2106.12864},
year = {2021}
}