AI System for Analyzing Medical Images Using Magnification-Aligned Transformers
This 2025 patent describes an AI system that analyzes medical images, like those from pathology slides, by using a special 'Transformer' model that understands how image details change with magnification.
Original patent title: “Method and system for analyzing pathological images based on magnification-aligned transformer (mat)”
This 2025 patent describes an AI system that analyzes medical images, like those from pathology slides, by using a special 'Transformer' model that understands how image details change with magnification. Owned by GUANGDONG PROVINCIAL PEOPLE'S HOSPITAL with 11 claims, and it is expected to expire in 2045.
Key facts
Coverage
What does this patent actually cover?
This patent details a method for analyzing pathological images, such as those from whole-slide images (WSIs) used in medical diagnosis. First, it identifies and isolates the actual tissue regions within these large images, discarding blank or unusable areas. Then, it breaks down these tissue regions into smaller 'patches.' The core of the invention is a 'Magnification-Aligned Transformer' (MAT) classification network model. This model has two main parts: one that aligns features from images taken at different magnifications (like zooming in or out) and another that uses both global (big picture) and local (detailed) attention mechanisms, combining the strengths of Transformers for overall understanding and Convolutional Neural Networks (CNNs) for fine details. This trained model then predicts a classification for the pathological image, helping doctors diagnose conditions. For example, it could help identify cancerous cells in a tissue sample.
The gap
What does this patent NOT cover?
- Analyzing images that are not pathological or medical in nature.
- Methods that do not involve breaking down whole-slide images into patches.
- AI models that do not use a Transformer architecture.
- Systems that do not specifically align features across different image magnifications.
- Analysis that relies solely on global features without considering local details.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The clever part is how the system aligns features extracted from images at different magnifications. It uses a self-supervised approach to ensure that the AI understands the same underlying tissue structures whether it's looking at a zoomed-out overview or a highly magnified view, preventing loss of critical detail during analysis.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
AI-assisted pathology slide analysis software
Digital pathology platforms
Computer-aided diagnosis systems for cancer detection
Why it matters
The bigger picture
This patent is significant because it addresses the challenge of analyzing complex medical images, particularly whole-slide pathology images, which are massive and contain crucial diagnostic information at various levels of detail. By developing an AI that can intelligently process these images across different magnifications, it aims to improve the accuracy and efficiency of disease diagnosis, potentially aiding pathologists in identifying subtle abnormalities.
Filed
November 25, 2025
Market context
Who's building on this
Companies in this space
As this patent was recently filed, it's too early to identify specific companies building directly on it. However, the field of AI in digital pathology is rapidly growing, with companies like Paige.AI and PathAI actively developing AI solutions for analyzing pathology slides. Major medical imaging companies are also investing in AI capabilities.
Market impact
This patent, if granted and broadly adopted, could significantly impact the digital pathology market by providing a more robust AI tool for analyzing complex whole-slide images. It has the potential to enhance diagnostic accuracy, speed up analysis times, and contribute to the standardization of AI-driven pathology workflows, potentially becoming a key technology for companies in this space.
Claim 1 — Plain English
What this patent covers
This patent details a method for analyzing pathological images, such as those from whole-slide images (WSIs) used in medical diagnosis. First, it identifies and isolates the actual tissue regions within these large images, discarding blank or unusable areas. Then, it breaks down these tissue regions into smaller 'patches.' The core of the invention is a 'Magnification-Aligned Transformer' (MAT) classification network model. This model has two main parts: one that aligns features from images taken at different magnifications (like zooming in or out) and another that uses both global (big picture) and local (detailed) attention mechanisms, combining the strengths of Transformers for overall understanding and Convolutional Neural Networks (CNNs) for fine details. This trained model then predicts a classification for the pathological image, helping doctors diagnose conditions. For example, it could help identify cancerous cells in a tissue sample.
The clever bit
The clever part is how the system aligns features extracted from images at different magnifications. It uses a self-supervised approach to ensure that the AI understands the same underlying tissue structures whether it's looking at a zoomed-out overview or a highly magnified view, preventing loss of critical detail during analysis.
What it does not cover
- Analyzing images that are not pathological or medical in nature.
- Methods that do not involve breaking down whole-slide images into patches.
- AI models that do not use a Transformer architecture.
- Systems that do not specifically align features across different image magnifications.
- Analysis that relies solely on global features without considering local details.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Limited data
Citation count
0/40
No citations yet
Claim breadth
7/20
Moderate scope
Recency
0/20
Older than 20 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$24K – $77K
Midpoint $48K · 19.4 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
11 claims as filed with the patent office.
Concepts involved
Cite this patent
HAN, C., LIU, Z., ZHAO, B., LIN, J., HUANG, Y., & Shi, Z. AI System for Analyzing Medical Images Using Magnification-Aligned Transformers (U.S. Patent No. 20,260,080,696). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20260080696/method-and-system-for-analyzing-pathological-images-based-on-magnification-align
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does AI System for Analyzing Medical Images Using Magnification-Aligned Transformers cover?
This 2025 patent describes an AI system that analyzes medical images, like those from pathology slides, by using a special 'Transformer' model that understands how image details change with magnification.
Who owns patent US 20260080696?
This patent is owned by GUANGDONG PROVINCIAL PEOPLE'S HOSPITAL.
When does this patent expire?
This patent is expected to expire on November 25, 2045, when the invention enters the public domain.
What problem does this patent solve?
This patent is significant because it addresses the challenge of analyzing complex medical images, particularly whole-slide pathology images, which are massive and contain crucial diagnostic information at various levels of detail. By developing an AI that can intelligently process these images across different magnifications, it aims to improve the accuracy and efficiency of disease diagnosis, potentially aiding pathologists in identifying subtle abnormalities.
What does this patent NOT cover?
Analyzing images that are not pathological or medical in nature.
Patent monitoring