Computer-Aided Diagnosis in the Era of Deep Learning

By Heang-Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala. Medical Physics (Lancaster), 2020

297 citations recorded by the source corpus.

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 non-imaging patient data and make 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.

Subfields: Artificial Intelligence, Health Information Management, Neurology

View Paper: https://www.semanticscholar.org/paper/a86b61da990253185067781ac774a1a3a4603a86

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In Artificial Intelligence

In Health Information Management

Ronald M. Summers

In Neurology

Citations in the corpus

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “5) Computer-aided detection (CADe) and diagnosis (CADx) [17]: CADe aims to localize or find a bounding box that contains an object (typically a lesion) of interest.”