University of California

William Hsu

Corey W Arnold

Shiwen Shen

Dieter R Enzmann

Kyung Hyun Sung

Xinran Zhong

Alex A T Bui

An automatic classification method for distinguishing between indolent and clinically significant carcinoma using multiparametric MRI (mp-MRI) imaging is provided. By utilizing a convolutional neural network (CNN), which automatically extracts deep features, the hierarchical classification framework avoids deficiencies in current schemes in the art such as the need to provide handcrafted features predefined by a domain expert and the precise delineation of lesion boundaries by a human or computerized algorithm. This hierarchical classification framework is trained using previously acquired mp-MRI data with known cancer classification characteristics and the framework is applied to mp-MRI images of new patients to provide identification and computerized cancer classification results of a suspicious lesion.

Deep-learning-based cancer classification using a hierarchical classification framework

A system is disclosed using a data-driven approach to objectively measure the diagnostic accuracy and value of diagnostic imaging reports using data captured routinely as part of the electronic health record. The system further utilizes the evaluation of the diagnostic accuracy of individual radiologists (imagers), subspecialty sections, modalities, and entire departments based on a comparison against a 'precision diagnosis' rendered by other clinical data sources such as pathology, surgery, laboratory tests, etc.

Automated quality control of diagnostic radiology

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Westlake Village, CA, United States of America

William Hsu