- Patent Title: Systems, methods, and apparatuses for learning semantics-enriched representations via self-discovery, self-classification, and self-restoration in the context of medical imaging
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Application No.: US17180575Application Date: 2021-02-19
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Publication No.: US11763952B2Publication Date: 2023-09-19
- Inventor: Fatemeh Haghighi , Mohammad Reza Hosseinzadeh Taher , Zongwei Zhou , Jianming Liang
- Applicant: Arizona Board of Regents on Behalf of Arizona State University
- Applicant Address: US AZ Scottsdale
- Assignee: Arizona Board of Regents on behalf of Arizona State University
- Current Assignee: Arizona Board of Regents on behalf of Arizona State University
- Current Assignee Address: US AZ Scottsdale
- Agency: Elliott, Ostrander & Preston, P.C.
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G16H50/70 ; G16H30/40 ; G16H30/20 ; G06F16/55 ; G06N3/08 ; G06F16/583 ; G06F18/28 ; G06F18/214 ; G06V10/772 ; G06V10/82

Abstract:
Described herein are means for learning semantics-enriched representations via self-discovery, self-classification, and self-restoration in the context of medical imaging. Embodiments include the training of deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a collection of semantics-enriched pre-trained models, called Semantic Genesis. Other related embodiments are disclosed.
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