Classification of Combined MLO and CC Mammographic Views Using Vision–Language Models
Bibliographic record
Abstract
Background: Breast cancer remains one of the leading causes of cancer-related deaths among women globally. Early detection through mammographic screening significantly improves survival rates, but the interpretation of mammograms is time-consuming and requires extensive expertise. Methods: We utilized six publicly available datasets, preprocessing paired craniocaudal (CC) and mediolateral oblique (MLO) views into dual-view concatenated images. Three vision-language models (VLMs)—Quantized Qwen2-VL-2B, Quantized SmolVLM (Idefics3-based), and MammoCLIP—were evaluated using two adaptation strategies: full supervised fine-tuning (SFT) and Linear Probing (LP). EfficientNet-B4 served as a CNN baseline. Results: Experiments show that while EfficientNet-B4 achieved the highest F1-score (0.5810), VLMs delivered competitive results with additional report generation capability. MammoCLIP exhibited the best VLM performance (F1 = 0.4755, ROC-AUC = 0.6906) under LP, outperforming general-purpose VLMs, which struggled with recall despite high precision. SmolVLM demonstrated balanced performance under full fine-tuning (F1 = 0.5101, ROC-AUC = 0.6304), indicating strong adaptability in resource-efficient setups. Conclusion: These findings highlight that domain-specific pretraining significantly enhances VLM effectiveness in mammography classification. Beyond classification, VLMs enable structured reporting and interactive decision support, offering promising avenues for clinical integration despite slightly lower predictive performance compared to specialized CNNs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".