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- Title
BMRMIA: A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System.
- Authors
Yankun Cao; Lina Xu; Zhi Liu; Xiaoyan Xiao; Mingyu Wang; Qin Li; Hongji Xu; Geng Yang
- Abstract
Contribution: This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology. The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system, and strengthen the teaching practice of deep learning related courses in hospitals, so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis. Background: In recent years, deep learning has been widely used in academia, industry, and medicine. An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning. Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques. For now, however, there is no practical training platform that can help radiologists learn automated medical image analysis systematically. Application Design: The platform proposes the basic learning, model combat, business application (BMR) concept, including the learning guidance system and the assessment training system, which constitutes a closed-loop learning guidance mode of "learning-assessment-training-learning". Findings: The survey results show that most of radiologists met their learning expectations by using this platform. The platform can help radiologists master deep learning techniques quickly, comprehensively and firmly.
- Subjects
DECISION support systems; COMPUTER-assisted image analysis (Medicine); IMAGE analysis; DIMENSIONAL analysis; DIAGNOSTIC imaging
- Publication
CMES-Computer Modeling in Engineering & Sciences, 2022, Vol 131, Issue 2, p851
- ISSN
1526-1492
- Publication type
Article
- DOI
10.32604/cmes.2022.018424