Invention Grant
- Patent Title: Machine learning using distance-based similarity labels
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Application No.: US17431334Application Date: 2020-03-26
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Publication No.: US12026875B2Publication Date: 2024-07-02
- Inventor: Eldad Klaiman , Jacob Gildenblat
- Applicant: HOFFMANN-LA ROCHE INC.
- Applicant Address: US NJ Little Falls
- Assignee: Hoffmann-La Roche, Inc.
- Current Assignee: Hoffmann-La Roche, Inc.
- Current Assignee Address: US NJ Little Falls
- Agency: Harness, Dickey & Pierce, P.L.C.
- Priority: EP 165965 2019.03.28
- International Application: PCT/EP2020/058570 2020.03.26
- International Announcement: WO2020/193708A 2020.10.01
- Date entered country: 2021-08-16
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06N3/045 ; G06V10/44 ; G06V10/74 ; G06V10/764 ; G06V10/774 ; G06V10/82 ; G06V20/69 ; G16H30/40

Abstract:
The method includes receiving a plurality of digital images each depicting a tissue sample; splitting each of the received images into a plurality of tiles; automatically generating tile pairs, each tile pair having assigned a label being indicative of the degree of similarity of two tissue patterns depicted in the two tiles of the pair, wherein the degree of similarity is computed as a function of the spatial proximity of the two tiles in the pair, wherein the distance positively correlates with dissimilarity; and training a machine learning module—MLM—using the labeled tile pairs as training data to generate a trained MLM, the trained MLM being configured for performing an image analysis of digital histopathology images.
Public/Granted literature
- US20220139072A1 MACHINE LEARNING USING DISTANCE-BASED SIMILARITY LABELS Public/Granted day:2022-05-05
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