International Business Machines Corporation

Merative US L.p.

Deepta Rajan

David James Beymer

Ehsan Dehghan Marvast

Luyao Shi

Tanveer Fathima Syeda-Mahmood

Ahmet Serkan Ozcan

Chaitanya Shivade

Tomasz Kornuta

Alexis Asseman

Methods and systems for training computer-aided condition detection systems. One method includes receiving a plurality of images for a plurality of patients, some of the images including an annotation associated with a condition; iteratively applying a first deep learning network to each of the images to produce an attention map, a feature map, and an image-level probability of the condition for each of the images; iteratively applying a second deep learning network to each feature map produced by the first network to produce a plurality of outputs; training the first network based on the attention map produced for each image; and training the second network based on the output produced for each of the patients. The second network includes a plurality of convolution layers and a plurality of convolutional long short-term memory (LSTM) layers. Each of the outputs includes a patient-level probability of the condition for one of the patients.

Automatic condition diagnosis using an attention-guided framework

Methods and systems for training computer-aided condition detection systems. One method includes receiving a plurality of images for a plurality of patients, some of the images including an annotation associated with a condition; iteratively applying a first deep learning network to each of the images to produce a segmentation map, a feature map, and an image-level probability of the condition for each of the images; iteratively applying a second deep learning network to each feature map produced by the first network to produce a plurality of outputs; training the first network based on the segmentation map produced for each image; and training the second network based on the output produced for each of the patients. The second network includes a plurality of convolution layers and a plurality of convolutional long short-term memory (LSTM) layers. Each of the outputs includes a patient-level probability of the condition for one of the patients.

Automatic condition diagnosis using a segmentation-guided framework

According to embodiments of the present disclosure, methods of and computer program products for operating a plurality of classifiers are provided. A plurality of input entities are read, each input entity having an associated target label. The input entities are provided to a first classifier, and a category of each input entity is obtained therefrom. A feature map is determined for each input entity. Each feature map is provided to each of a set of classifiers, and an assigned label is obtained for each feature map from each of the set of classifiers. Each classifier is associated with one of the categories. For each classifier, the assigned label for each feature map is compared to the target labels to determine a plurality of gradients. The plurality of gradients are masked according to each category, yielding a masked set of gradients for each category. Each classifier is trained according its associated masked gradients.

Variational gradient flow

Automatic discrepancy detection in medical data is provided. In various embodiments, a disease label for a present study is determined, indicative of a disease condition. Retrospective review of a plurality of electronic medical records is performed. The retrospective review comprises searching for electronic medical records relevant to the disease condition. The earliest electronic medical record reflecting the disease condition is identified. Based on the earliest electronic medical record reflecting the disease condition, one or more of the electronic medical records having an omission or inconsistency is identified. The one or more of the electronic medical records having an omission or inconsistency are flagged for supplemental review in a worklist.

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San Jose, CA, United States of America

Deepta Rajan