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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Cross-slide augmentation for whole slide image classification based on class activation map.

Yanjia Chen¹, Hejun Wu¹, Ziwang Huang¹

¹Sun Yat - sen University, No.132, Outer Ring East Road, Panyu District, Guangzhou, 511400, Guangdong, China.

Scientific Reports

|November 26, 2025

Summary

This study introduces novel Whole Slide Image (WSI) classification methods using Class Activation Map (WSICAM) and Cross-Slide Augmentation (CSA) to improve cancer diagnosis and tumor localization by addressing attention score inaccuracies and model overfitting.

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Area of Science:

Computational pathology
Medical image analysis
Artificial intelligence in oncology

Background:

Weakly supervised Multiple Instance Learning (MIL) is crucial for Whole Slide Image (WSI) classification due to gigapixel resolution.
Attention-based MIL methods show promise in cancer diagnosis but struggle with accurate instance contribution scoring and model overfitting.

Purpose of the Study:

To enhance discriminative region identification in WSIs by accurately representing instance contributions.
To mitigate model overfitting and improve positive sample representation in whole-slide pathological image analysis.

Main Methods:

A novel module utilizing Class Activation Map suitable for WSI (WSICAM) was designed to determine accurate instance contribution weights.
A Cross-Slide Augmentation (CSA) module was implemented to create new training samples by mixing labels based on discriminative instances.
The proposed framework integrates two WSICAM modules and one CSA module for improved WSI classification.

Main Results:

The developed framework achieved state-of-the-art performance in WSI classification across multiple benchmark datasets.
Visualizations confirmed the method's effectiveness in identifying discriminative regions within WSIs.
The approach demonstrated robust capabilities in precise tumor lesion localization.

Conclusions:

The proposed WSICAM and CSA modules effectively address limitations in existing attention-based MIL methods for WSI classification.
This framework offers a significant advancement in computational pathology for accurate cancer diagnosis and treatment planning.
The method shows strong potential for clinical application in identifying and localizing cancerous regions in pathological images.