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Updated: Apr 19, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Pathology-Aligned Contrastive Representation Learning for Gleason Grading.

Maoye Huang, Jing Zhong, Jiawei Wu

IEEE Transactions on Medical Imaging

|April 17, 2026

Summary

This summary is machine-generated.

Automated prostate cancer grading needs better methods. Pathology-Aligned Contrastive Representation Learning (PA-CRL) improves self-supervised learning for pathology images, enhancing Gleason grading accuracy.

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Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

Area of Science:

Digital pathology
Computational oncology
Machine learning in healthcare

Background:

Gleason grading for prostate cancer is subjective, leading to variability.
Automated systems are needed but lack sufficient annotated pathology data.
Existing self-supervised learning (SSL) methods fail to capture pathology-specific features.

Purpose of the Study:

To develop a novel SSL method for pathology images.
To improve the accuracy of automated Gleason grading.
To address the limitations of current SSL approaches in capturing fine-grained morphological details.

Main Methods:

Proposed Pathology-Aligned Contrastive Representation Learning (PA-CRL).
Introduced a diagnostic-aware masking strategy for pathology-relevant regions.
Implemented mask-driven entropic label smoothing (MDELS) for representation stability.

Main Results:

PA-CRL learns pathology-relevant representations effectively.
Achieved a 2.89% F1-score improvement in Gleason Grade Group evaluation.
Outperformed state-of-the-art methods on multiple datasets.

Conclusions:

PA-CRL offers a robust solution for learning from unlabeled pathology data.
The method enhances automated Gleason grading accuracy.
PA-CRL advances the application of SSL in digital pathology.