The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 19, 2025
Filed:
Mar. 24, 2023
Arizona Board of Regents on Behalf of Arizona State University, Scottsdale, AZ (US);
Dongao Ma, Tempe, AZ (US);
Jiaxuan Pang, Tempe, AZ (US);
Nahid Ul Islam, Mesa, AZ (US);
Mohammad Reza Hosseinzadeh Taher, Tempe, AZ (US);
Fatemeh Haghighi, Tempe, AZ (US);
Jianming Liang, Scottsdale, AZ (US);
Arizona Board of Regents on behalf of Arizona State University, Scottsdale, AZ (US);
Abstract
Described herein are systems, methods, and apparatuses for implementing self-supervised domain-adaptive pre-training via a transformer for use with medical image classification in the context of medical image analysis. An exemplary system includes means for receiving a first set of training data having non-medical photographic images; receiving a second set of training data with medical images; pre-training an AI model on the first set of training data with the non-medical photographic images; performing domain-adaptive pre-training of the AI model via self-supervised learning operations using the second set of training data having the medical images; generating a trained domain-adapted AI model by fine-tuning the AI model against the targeted medical diagnosis task using the second set of training data having the medical images; outputting the trained domain-adapted AI model; and executing the trained domain-adapted AI model to generate a predicted medical diagnosis from an input image not present within the training data.