公开(公告)号：US20240078666A1

公开(公告)日：2024-03-07

申请号：US18241809

申请日：2023-09-01

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Jiaxuan PANG ,  Fatemeh Haghighi ,  DongAo Ma ,  Nahid Ui Islam ,  Mohammad Reza Hosseinzadeh Taher ,  Jianming Liang

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/54 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06V10/54 ,  G16H30/40 ,  G06T2207/20081 ,  G06V2201/03

Abstract: A self-supervised machine learning method and system for learning visual representations in medical images. The system receives a plurality of medical images of similar anatomy, divides each of the plurality of medical images into its own sequence of non-overlapping patches, wherein a unique portion of each medical image appears in each patch in the sequence of non-overlapping patches. The system then randomizes the sequence of non-overlapping patches for each of the plurality of medical images, and randomly distorts the unique portion of each medical image that appears in each patch in the sequence of non-overlapping patches for each of the plurality of medical images. Thereafter, the system learns, via a vision transformer network, patch-wise high-level contextual features in the plurality of medical images, and simultaneously, learns, via the vision transformer network, fine-grained features embedded in the plurality of medical images.