IP Library › Granted Patent US 12,639,932
Granted Patent B2
US 12,639,932 · App. 18/528,675 · Granted May 26, 2026

Systems, methods, and apparatuses for learning foundation models from anatomy in medical imaging for use with medical image classification and segmentation

Inventors: Mohammad Reza Hosseinzadeh Taher (Tempe, AZ); Jianming Liang (Scottsdale, AZ)
Assignee: Arizona Board of Regents on Behalf of Arizona State University
G06V10/774G06T7/0012G06V2201/03
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Quick Facts
Patent No.
US 12,639,932
App. No.
18/528,675
Granted
May 26, 2026
Kind
B2
Abstract

Human anatomical structures are extracted from medical images. A foundation model is generated by learning the human anatomical structures, resulting in generic representations of the human anatomical structures. The human anatomical structures are learned by executing a self-supervised contrastive learning framework to conserve hierarchical relationships of the human anatomical structures in the medical images and to capture distinct representations for different human anatomical structures at different granularity levels. The self-supervised contrastive learning framework executes an anatomy decomposer (AD) to conserve hierarchical relationships of the human anatomical structures in the received medical images and executes a purposive pruner (PP) to capture distinct representations for different human anatomical structures at different granularity levels. Medical images that form no part of the received medical images may then be processed using the trained foundation model.

Claims (32)

1 . A method comprising:

receiving medical images;

extracting human anatomical structures from the received medical images;

training a foundation model, via a self-supervised machine learning process that learns the human anatomical structures, resulting in generic representations of the human anatomical structures, wherein the self-supervised machine learning process that learns the human anatomical structures comprises:

executing a self-supervised contrastive learning framework to conserve hierarchical relationships of the human anatomical structures in the received medical images and to capture distinct representations for different human anatomical structures at different granularity levels, and wherein executing the self-supervised contrastive learning framework comprises:

executing an anatomy decomposer (AD) that decomposes anatomy in the received medical images into hierarchical relationships of the human anatomical structures at different granularity levels; and

executing a purposive pruner (PP) to capture distinct representations of the human anatomical structures at different granularity levels; and

processing, using the generated foundation model, medical images that form no part of the received medical images used in training the generated foundation model.

2 . The method of claim 1 , wherein the self-supervised machine learning process that learns the human anatomical structures comprises the self-supervised machine learning process learning prominent objects in the received medical images corresponding to the human anatomical structures.

3 . The method of claim 2 , wherein the self-supervised machine learning process that learns the human anatomical structures further comprises the self-supervised machine learning process learning detailed parts within the learned prominent objects corresponding to sub-portions of the generic representations of the human anatomical structures.

4 . A system comprising:

a memory to store instructions;

a processor to execute the instructions stored in the memory to perform the following operations:

receiving medical images;

extracting human anatomical structures from the received medical images;

training a foundation model, via a self-supervised machine learning process that learns the human anatomical structures, resulting in generic representations of the human anatomical structures, wherein the self-supervised machine learning process that learns the human anatomical structures comprises:

executing a self-supervised contrastive learning framework to conserve hierarchical relationships of the human anatomical structures in the received medical images and to capture distinct representations for different human anatomical structures at different granularity levels, and wherein executing the self-supervised contrastive learning framework comprises:

executing an anatomy decomposer (AD) that decomposes anatomy in the received medical images into hierarchical relationships of the human anatomical structures at different granularity levels; and

executing a purposive pruner (PP) to capture distinct representations of the human anatomical structures at different granularity levels; and

processing, using the generated foundation model, medical images that form no part of the received medical images used in training the generated foundation model.

5 . The system of claim 4 , wherein the self-supervised machine learning process that learns the human anatomical structures comprises the self-supervised machine learning process learning prominent objects from within the medical images received corresponding to the human anatomical structures.

6 . The system of claim 5 , wherein the self-supervised machine learning process that learns the human anatomical structures further comprises the self-supervised machine learning process learning detailed parts within the learned prominent objects corresponding to sub-portions of the generic representations of the human anatomical structures.

7 . A non-transitory computer readable storage media having instructions stored thereupon that, when executed by a system having at least a processor and a memory therein, the instructions cause the processor to perform operations including:

receiving medical images;

extracting human anatomical structures from the received medical images;

training a foundation model, via a self-supervised learning process that learns the human anatomical structures, resulting in generic representations of the human anatomical structures, wherein the self-supervised learning process that learns the human anatomical structures comprises:

executing a self-supervised contrastive learning framework to conserve hierarchical relationships of the human anatomical structures in the received medical images and to capture distinct representations for different human anatomical structures at different granularity levels, and wherein executing the self-supervised contrastive learning framework comprises:

executing an anatomy decomposer (AD) that decomposes anatomy in the received medical images into hierarchical relationships of the human anatomical structures at different granularity levels; and

executing a purposive pruner (PP) to capture distinct representations of the human anatomical structures at different granularity levels; and

processing, using the generated foundation model, medical images that form no part of the received medical images used in training the generated foundation model.

8 . The non-transitory computer readable medium of claim 7 , wherein the self-supervised machine learning process that learns the human anatomical structures comprises the self-supervised machine learning process learning prominent objects in the received medical images corresponding to the human anatomical structures.

9 . The non-transitory computer readable medium of claim 8 , wherein the self-supervised machine learning process that learns the human anatomical structures further comprises the self-supervised machine learning process learning detailed parts within the learned prominent objects corresponding to sub-portions of the generic representations of the human anatomical structures.

Assignments (2)
LICENSE Recorded Jul 26, 2024
From: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
To: NATIONAL INSTITUTES OF HEALTH
Reel/Frame 068174/0560 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 5, 2023
From: HOSSEINZADEH TAHER, MOHAMMAD REZA; LIANG, JIANMING
To: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
Reel/Frame 065770/0155 →
Continuity (2)
Provisional Application 63430219 · Dec 5, 2022
Related Publication 20240290076A1 · Aug 29, 2024
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