Deep Learning Model for Invasive Ductal Carcinoma Detection in Histopathology Images
Bibliographic record
Abstract
Breast cancer, with Invasive Ductal Carcinoma (IDC) as its most prevalent subtype, presents substantial challenges in early detection and diagnosis. This study introduces a novel computer-aided diagnosis (CAD) system designed to detect IDC in histopathology images with high precision. Leveraging a convolutional neural network (CNN), the system incorporates advanced methodologies such as sliding window-based heatmaps and a unique oversampling technique that extracts patches from homogenous regions in stitched whole-slide images. This approach enhances minority class representation while maintaining biological integrity, addressing class imbalance effectively. The system achieved a balanced accuracy of 89.06% and an F1-score of 86.68%, outperforming existing models in the literature. The implementation of sliding heatmaps further improves interpretability, enabling pathologists to visualize model predictions seamlessly. This research demonstrates the potential of combining innovative deep learning techniques with domainspecific insights to enhance IDC detection, offering a reliable tool for clinical practice and contributing to improved patient outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".