Covera Health, Inc.

James Robert Browning

Benjamin L Odry

Richard J Herzog

Li Zhang

Daniel Robert Elgort

Ron Vianu

JinHyeong Park

Bryce Eakin

Benjamin Sellman Suutari

Gregory Allen Dubbin

Xiaojin Dong

Jayashri Pawar

Micha Kornreich

Tarmo Henrik Aijo

Aubrey Chow

Described are techniques for uncertainty-aware anatomical landmark detection, using, for example, a deep reinforcement learning (DRL) anatomical landmark detection agent. For instance, a process can include generating one or more image features for an input medical image using a first sub-network of the anatomical landmark detection agent. A softmax layer of a second sub-network of the anatomical landmark detection agent can generate a plurality of discrete Q-value distributions for a set of allowable actions associated with movement of the agent within the medical image. An anatomical landmark location within the medical image can be predicted using the discrete Q-value distributions. An uncertainty can be determined for the predicted anatomical landmark location, based on an average full width half maximum (FWHM) calculated for the plurality of discrete Q-value distributions.

Uncertainty-aware deep reinforcement learning for anatomical landmark detection in medical images

For training data pairs comprising training text (a radiological report) and training images (radiological images associated with the radiological report), a first encoder network determines word embeddings for the training text. A concept is generated from the operation of layers of the first encoder network, which is regularized by a first loss between the generated concept and a labeled concept for the training text. A second encoder network determines features for the training image. A heatmap is generated from the operation of layers of the second encoder network, which is regularized by a second loss between the generated heatmap and a labeled heatmap for the training image. A categorical cross entropy loss is calculated between a diagnostic quality category (classified by an error encoder) and a labeled diagnostic quality category for the training data pair. A total loss function comprising the first, second, and categorical cross entropy losses is minimized.

Computer-implemented machine learning for detection and statistical analysis of errors by healthcare providers

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Vauxhall, NJ, United States of America

James Robert Browning