Patch-based deep learning models for breast mammographic mass classification
Bibliographic record
Abstract
Background: Breast cancer is one of the greatest health threats to women worldwide. Mammography is an effective and inexpensive tool for breast cancer early detection. Mammography-based breast cancer screening requires a lot of manpower from professional experts. Thus, computer-aided diagnosis tools, especially accurate classifiers which can distinguish the breast masses from the background tissues, are needed. However, since the sample size of publicly available mammography data sets is relatively small, the performance of the published breast mass identification models was not great, and the models were not well-embraced by clinical practice due to their low interpretability. Methods: In this work, using two independent and well-known mammography data sets, the CBIS-DDSM and the INbreast, we proposed a novel patch generation method for data augmentation and negative case generation. We implemented two successful deep learning models, the ResNet and the ViT, to classify the generated mass and non-mass patches. We also proposed to apply the patch-level model to the full-view mammogram screening in a sliding window manner and visualize/interpret the prediction results using a heatmap so that the clinic practice could potentially benefit from the well-trained model. Result: For the CBIS-DDSM dataset, we compared the performance of the ResNet and the ViT with and without data augmentation. The F1 score is 0.91, 0.86, 0.85, and 0.70, respectively. We also evaluated our models using other metrices such as accuracy, precision, recall, and ROC curve. The results show that the ResNet model outperforms the ViT model. And the data augmentation improves the overall performance of the models. The similar conclusions are further supported using the independent INbreast data. Furthermore, we also explored to use probability-based heatmaps to visualize the potential mass regions in mammogram images. Conclusion: The study shows that our patch-level data augmentation is effective in improving the classification performance of the deep learning models. The comparable performance on the CBIS-DDSM data and the independent INbreast data demonstrates the generalizability of our methods. The proposed heatmap visualization tool increases the interpretability of our results and could be a potential approach for clinic utilization.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".