Computer‐aided diagnosis in the era of deep learning. Issue 5 (17th May 2020)

• Record Type: Journal Article
• Authors: Chan, Heang‐Ping; Hadjiiski, Lubomir M.; Samala, Ravi K.
• Other Names: El Naqa Issam guestEditor.; Das Shiva K. guestEditor.
• Is Part Of: Medical physics. Volume 47:Issue 5(2020)
• Page Start: e218
• Page End: e227
• Publication Date: 2020-05-17
• Subjects: artificial intelligence -- computer‐aided diagnosis -- deep learning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13764
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Computer‐aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then be used to assist clinicians in their decision‐making process. The recent success of the deep learning technology in machine learning spurs new research and development efforts to improve CAD performance and to develop CAD for many other complex clinical tasks. In this paper, we discuss the potential and challenges in developing CAD tools using deep learning technology or artificial intelligence (AI) in general, the pitfalls and lessons learned from CAD in screening mammography and considerations needed for future implementation of CAD or AI in clinical use. It is hoped that the past experiences and the deep learning technology will lead to successful advancement and lasting growth in this new era of CAD, thereby enabling CAD to deliver intelligent aids to improve health care.