公开(公告)号：US11645833B2

公开(公告)日：2023-05-09

申请号：US17528566

申请日：2021-11-17

Applicant: MERATIVE US L.P.

Inventor： Ali Madani ,  Mehdi Moradi ,  Tanveer F. Syeda-Mahmood

IPC: G16H30/40 ,  G16H30/20 ,  G06T7/00 ,  A61B6/00 ,  G06N5/02 ,  G06N20/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06N5/022 ,  G06N3/082

CPC classification number: G06K9/6259 ,  A61B6/5217 ,  G06K9/6267 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/0481 ,  G06N3/082 ,  G06N5/022 ,  G06N20/00 ,  G06T7/0014 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/10116 ,  G06T2207/30048

Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

公开(公告)号：US11922682B2

公开(公告)日：2024-03-05

申请号：US17221146

申请日：2021-04-02

Applicant: MERATIVE US L.P.

Inventor： Mehdi Moradi ,  Chun Lok Wong

IPC: G06V10/82 ,  G06F18/24 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06N5/01 ,  G06N20/00 ,  G06N20/10 ,  G06N20/20 ,  G06T7/00 ,  G06V10/764 ,  G06V20/69 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70

CPC classification number: G06V10/82 ,  G06F18/24 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/0014 ,  G06V10/764 ,  G06V20/69 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G06N3/044 ,  G06N3/047 ,  G06N5/01 ,  G06N20/10 ,  G06N20/20 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30004 ,  G06T2207/30048 ,  G06V2201/03

Abstract: Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.

公开(公告)号：US11763931B2

公开(公告)日：2023-09-19

申请号：US16377944

申请日：2019-04-08

Applicant: MERATIVE US L.P.

Inventor： Alexandros Karargyris ,  Chun Lok Wong ,  Joy Wu ,  Mehdi Moradi

IPC: G06T7/00 ,  G16H30/40 ,  G06N20/00 ,  G16H15/00 ,  G06F40/169 ,  G06F18/24 ,  G06F18/21 ,  G06F18/214 ,  G06V10/774 ,  G06V10/776 ,  G06F40/30

CPC classification number: G16H30/40 ,  G06F18/217 ,  G06F18/2148 ,  G06F18/24 ,  G06F40/169 ,  G06N20/00 ,  G06T7/0012 ,  G06V10/774 ,  G06V10/776 ,  G16H15/00 ,  G06F40/30 ,  G06T2207/10116 ,  G06T2207/20081

Abstract: Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.