Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions—Abdominal Imagers' Perspective. Issue 6 (14th November 2021)

• Record Type: Journal Article
• Authors: Yedururi, Sireesha; Morani, Ajaykumar C.; Katabathina, Venkata Subbiah; Jo, Nahyun; Rachamallu, Medhini; Prasad, Srinivasa; Marcal, Leonardo
• Is Part Of: Journal of computer assisted tomography. Volume 45:Issue 6(2021)
• Page Start: 805
• Page End: 811
• Publication Date: 2021-11-14
• Subjects: machine learning -- artificial intelligence -- oncologic imaging -- standard annotations -- universal annotations; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001183
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract: The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imaging, including the lack of availability of a large number of annotated data sets and lack of use of consistent methodology and terminology for reporting the findings observed on the staging and follow-up imaging studies that apply to a wide spectrum of solid tumors. This short review discusses some potential hurdles to the implementation of machine learning in oncologic imaging, opportunities for improvement, and potential solutions that can facilitate robust machine learning from the vast number of radiology reports and annotations generated by the dictating radiologists.