International Business Machines Corporation

Yale University

Merative US L.p.

Luyao Shi

David James Beymer

Ehsan Dehghan Marvast

Deepta Rajan

Hui Liu

Chi Liu

John Onofrey

A system for estimating attenuation coefficients from only single photon emission computed tomography (SPECT) emission data using deep neural networks includes an artificial neural network based upon machine learning system estimating attenuation maps for SPECT emission data, and associated attenuation correction method.

Method and system for generating attenuation map from SPECT emission data

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

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New Haven, CT, United States of America

Luyao Shi