Deep Learning-Based Melanoma Detection UsingAttention Maps
Bibliographic record
Abstract
Melanoma is a cancer type with notably high fatality rates whose prevalence has been increasing in the last two decades and is forecasted to be even more prevalent in the future. Consequently, the development of non-invasive and highly efficient diagnostic methods tailored to melanoma is urgently needed. Among the methods employed for diagnosis is the examination of dermoscopic images of skin lesions, which can also be analyzed via Deep Learning, as previous scholarly works have shown. We present an attention-based framework predicated upon the supposition that the lesion is the most informative part of dermoscopic images. As a result, the proposed method performs the task of lesion localization before classification. Subsequently, leveraging a Deep Learning-driven feature extractor, black-box features are obtained for the lesion segment, complemented by a feature extractor to extract features from the entire image. A composite feature vector is then obtained by concatenating the two feature vectors and, upon processing through a simple neural network, enables image classification. Empirical validation conducted on the ISIC 2019 dataset with the proposed methodology yielded an accuracy of 75.5%.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".