The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 16, 2024

Filed:

May. 26, 2021
Applicant:

Merative Us L.p., Ann Arbor, MI (US);

Inventors:

Luyao Shi, New Haven, CT (US);

David James Beymer, San Jose, CA (US);

Ehsan Dehghan Marvast, Palo Alto, CA (US);

Deepta Rajan, San Jose, CA (US);

Assignee:

Merative US L.P., Ann Arbor, MI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); G06F 18/21 (2023.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); G06F 18/217 (2023.01); G06T 7/0014 (2013.01); G16H 30/40 (2018.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30061 (2013.01); G06T 2207/30101 (2013.01);
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.


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