Lightweight Hybrid Transformer System for Robust and Explainable Multi-Modality Breast Cancer Recognition
Bibliographic record
Abstract
Breast cancer is a major global health concern, making early detection crucial for improving survival rates. Deep learning methods in this field face challenges such as class imbalance, varying imaging types, and clinical interpretability. This study introduces a lightweight transformer-based model, EFormer-EA, aimed at breast cancer classification across different imaging modalities. Our hybrid architecture uses EfficientFormerV2 for local feature extraction and External Attention for modeling global dependencies. We applied it to two public datasets: BreakHis, containing 7,909 histopathology images at four magnifications, and BUSI, with 830 ultrasound images across three classes. Preprocessing included modality-specific normalization, histogram equalization, and GPU-accelerated augmentation, with a class-weighted loss to address class imbalance. The EFormer-EA model achieved impressive results: an F1 score of 98.27% and a Matthew's correlation coefficient of 96.15 on BreakHis, and an F1 score of 98.46% and a PR-AUC of 99.43 on BUSI, outperforming existing models. We also incorporated Grad-CAM into a web application for real-time, explainable diagnosis. However, the study has limitations, including sensitivity to external memory sizes and dependence on just two datasets. Future work will focus on multi-center validation, edge deployment, and integration with federated learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".