Breast Cancer Detection from Thermal Images Using Asymmetries in Learned Texture Vectors
Bibliographic record
Abstract
Breast cancer is the most widespread form of cancer among women. While mammography is the gold standard for diagnosis, it is often considered troublesome for the patients. Thermal imaging, on the other hand, has the power to detect early-stage breast cancer without producing radiation or necessitating direct contact with the patient. Since the affected side has an increase in temperature, it is possible to compare the left and right breasts to distinguish healthy cases from malignant ones. We propose a method based on the swapping autoencoder to learn a texture representation that is disentangled from structure and used to effectively detect left-right differences in a classification task. We trained and evaluated our model on the public DMR-IR dataset, in a low data regime compared to existing work with swapping autoencoders. Our model obtained 0.962 accuracy, an F1 score of 0.971 and 0.988 AUROC. It thus achieves competitive results with state-of-the-art methods, but needs fewer parameters and has lower complexity. The code is available through https://github.com/VisionICLab/TADA-SAE.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".