公开(公告)号：US11984206B2

公开(公告)日：2024-05-14

申请号：US16958544

申请日：2018-02-16

Applicant: GOOGLE LLC

Inventor： Scott McKinney ,  Shravya Shetty ,  Hormuz Mostofi

IPC: G16H15/00 ,  G06N3/08 ,  G16H30/40

CPC classification number: G16H15/00 ,  G06N3/08 ,  G16H30/40

Abstract: A method is provided for processing medical text and associated medical images. A natural language processor configured as a deep conventional neural network is trained on a first corpus of curated free-text, medical reports each of which having one or more structured labels assigned by an medical expert. The network is trained to learn to read additional free-text medical reports and produce predicted structured labels. The natural language processor is applied to a second corpus of free-text medical reports that are associated with medical images. The natural language processor generates structured labels for the associated medical images. A computer vision model is trained using the medical images and the structured labels generated. The computer vision model can thereafter assign a structured label to a further input medical image. In one example, the medical images are chest X-rays.

公开(公告)号：US11922668B2

公开(公告)日：2024-03-05

申请号：US17520538

申请日：2021-11-05

Applicant: Google LLC

Inventor： Rebecca Ackermann ,  William Chen ,  Thad Hughes ,  Teagan Daly ,  Scott McKinney ,  Rory Sayres ,  Quang Duong ,  Jacob Stimes ,  Eric Lindley ,  Cristhian Cruz ,  Beverly Freeman

IPC: G06V10/22 ,  G06F3/04815 ,  G06F3/0484 ,  G16H30/40

CPC classification number: G06V10/235 ,  G06F3/04815 ,  G06F3/0484 ,  G16H30/40

Abstract: A set of user interface tools is described facilitating asynchronous adjudication of one or more regions-of-interest in a medical image by a group of two or more graders, each of which has access to the set of tools in a workstation environment. The set of tools include (1) a feature for enabling the graders to assess the medical image and manually delineate one or more specific regions-of-interest (ROI) in the medical image, (2) a feature for assessing the ROI(s) delineated by other graders, including display of the ROI delineated by other graders; (3) dialog features for explaining and discussing the assessments, including a text feature for discussing the assessments. The dialog features and the ROIs delineated by all the graders are visible to all the graders on the workstation display as they collectively adjudicate the medical image in a round-robin manner. The set of tools further include (4) a feature for manually verifying grader agreement with the other graders' assessments.

公开(公告)号：US20220254023A1

公开(公告)日：2022-08-11

申请号：US17597876

申请日：2020-06-16

Applicant: Google LLC

Inventor： Scott McKinney ,  Marcin Sieniek ,  Varun Godbole ,  Shravya Shetty ,  Natasha Antropova ,  Jonathan Godwin ,  Christopher Kelly ,  Jeffrey De Fauw

IPC: G06T7/00 ,  G06V10/22 ,  G06V10/82 ,  G16H30/40 ,  G06V10/44 ,  G16H50/20

Abstract: A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image. One or more regions of interest (ROIs) are identified in one or more of the images in the set of images. For ROI identified, a reference region is identified in the associated counterpart image. ROIs and associated reference regions are cropped out, thereby forming cropped pairs of images 1 . . . n1, that are fed to a deep learning model trained to make a prediction of probability of a state of the ROI, e.g., disease state, which generates a prediction Pi-, (i=1 . . . n) for each cropped pair. The model generates an overall prediction P from each of the predictions Pi. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated.