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Embedding-driven dual-branch approach for accurate breast tumor cellularity classification.

Hossam Magdy Balaha¹, Ali Mahmoud¹, Khadiga M Ali²

¹Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, 40292, USA.

Scientific Reports

|November 20, 2025

Summary

This study introduces a dual-branch framework for accurate breast tumor cellularity classification from histopathology images. The model integrates embedding extraction and vision classification, achieving high accuracy and demonstrating the importance of each component.

Keywords:

Breast cancer (BC)Deep learning (DL)Embeddings Histopathological analysis Transformers Tumor cellularity classification

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Area of Science:

Medical Image Analysis
Computational Pathology
Artificial Intelligence in Oncology

Background:

Accurate breast tumor cellularity classification is crucial for effective cancer treatment and prognosis.
Histopathological images are key for evaluating cellularity, but manual assessment can be subjective and time-consuming.

Purpose of the Study:

To develop and validate a novel dual-branch framework for precise breast tumor cellularity classification using histopathological images.
To investigate the contribution of embedding-driven and vision-based approaches in cellularity classification.
To enhance feature extraction and mitigate overfitting through a Knowledge Block.

Main Methods:

A dual-branch framework integrating an Embedding Extraction Branch (Virchow2 transformation) and a Vision Classification Branch (Nomic AI Embedded Vision v1.5).
Combination of outputs from both branches for final classification.
Implementation of a Knowledge Block with fully connected layers, batch normalization, and dropout.
Utilized data augmentation techniques to improve model performance.

Main Results:

The proposed framework achieved high performance metrics, including accuracy, specificity, sensitivity, precision, and F1 score.
Ablation studies confirmed the essential role of the embedding extraction branch, with its removal significantly decreasing accuracy.
The vision classification branch also contributed significantly, with its removal leading to a smaller accuracy decrease.
Data augmentation was shown to enhance model performance.

Conclusions:

The dual-branch framework offers a robust and accurate method for breast tumor cellularity classification.
Both embedding extraction and vision classification components are vital for optimal performance.
The study highlights the potential of AI-driven approaches in digital pathology for improved cancer diagnosis.