Deepta O Rajan

US Patent:

20220384035, Dec 1, 2022

Filed:

May 26, 2021

Appl. No.:

17/331251

Inventors:

- Armonk NY, US
David James Beymer - San Jose CA, US
Ehsan Dehghan Marvast - Palo Alto CA, US
Deepta Rajan - San Jose CA, US

International Classification:

G16H 50/20
G06T 7/00
G06K 9/62
G16H 30/40

Abstract:

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.

US Patent:

20210357743, Nov 18, 2021

Filed:

May 12, 2020

Appl. No.:

16/872907

Inventors:

- Armonk NY, US
Ahmet Serkan Ozcan - Los Altos CA, US
Deepta Rajan - San Jose CA, US
Alexis Asseman - San Jose CA, US
Chaitanya Shivade - San Jose CA, US

International Classification:

G06N 3/08
G06N 3/04

Abstract:

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.