Johns Hopkins University

Siemens Healthcare Gmbh

Ihab Kamel

Shaohua Kevin Zhou

Atilla Peter Kiraly

Benjamin L Odry

David D Liu

Berthold Kiefer

Robert Grimm

Li Pan

Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.

Tissue characterization based on machine learning in medical imaging

Growing community of inventors

Ellicott City, MD, United States of America

Ihab Kamel