Deepskinformer: Skin Lesion Segmentation Using Hierarchical Transformers And Edge Enhancement
Bibliographic record
Abstract
Segmentation of skin lesions from dermatological images is critical in diagnosing and treating skin cancer. Despite this, the diversity of lesion shapes, sizes, and textures against a similar-toned skin backdrop makes these images challenging to analyze. Current segmentation methods are often less precise in delineating boundaries and more susceptible to interference from background noise. To address this issue, we introduce an end-to-end framework called DeepSkinFormer (DSF) for skin lesion segmentation using the Skin Edge Enhancement Module (SEEM) to enhance boundaries for efficient detection. We evaluate the proposed model on standard benchmarks, HAM10000, ISIC2017, and PH2 datasets. Our model outperforms existing methods and achieves stateof-the-art results using the Dice and mean Intersection Over Union (mIOU) scores. Furthermore, we conduct an ablation study to confirm the significant contributions of DSFspecialized modules to their effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".