Deep Learning for Breast Cancer Screening [AIMI-IBIIS Seminar Series]

Speaker(s): 

Hugh Harvey, MBBS, MD
Stanford community

Abstract:
The greatest issue in global breast screening is not accuracy, but a workforce crisis. Traditional computer-aided detection (CAD) for mammography decision-support could not reach the level of an independent reader. Deep learning (DL) outperforms CAD and is close to surpassing human performance. DL is already capable of decision support and density assessment for 2D full-field digital mammography (FFDM), and is on the cusp of providing consistent, accurate and interpretable mammography reading as an independent reader.

About:
Dr. Harvey is a board certified radiologist and clinical academic, trained in the NHS and Europe’s leading cancer research institute, the ICR, where he was twice awarded Science Writer of the Year. Previously a consultant radiologist at Guys & St Thomas’, London, he serves as a Royal College of Radiologists informatics committee and AI advisory board member, the clinical director at Kheiron Medical an AI company focussed on deep learning in breast cancer, associate editor at Nature: Digital Medicine, and also co-chair of the government Prof Topol’s Review into AI in healthcare.