In:
ACM Computing Surveys, Association for Computing Machinery (ACM)
Abstract:
The astounding success made by artificial intelligence 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. Many junior researchers faced a lack of data, because of a variety of reasons. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require several other resources, such as professional equipment and expertise. That makes it difficult for novice and non-medical researchers to have access to 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 the information of around three hundred datasets and challenges mainly reported between 2007 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood, and “others”. The purpose of our paper is to provide a list, as up-to-date and complete as possible, that can be used as a reference to easily find the datasets for medical image analysis and the information related to these datasets.
Type of Medium:
Online Resource
ISSN:
0360-0300
,
1557-7341
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2023
detail.hit.zdb_id:
215909-0
detail.hit.zdb_id:
1495309-2
detail.hit.zdb_id:
626472-4
