Deep learning in dermatopathology: applications for skin disease diagnosis and classification
Bibliographic record
Abstract
Medical image segmentation is pivotal in disease diagnosis and treatment planning across various imaging modalities, including MRI, CT, ultrasound, X-ray, dermoscopy, and histopathology. This systematic literature review, conducted using the PRISMA framework, provides a comprehensive analysis of Deep Learning approaches applied to medical image segmentation, with a focus on dermato-pathology for skin disease diagnosis and classification. Transformer-based models have shown notable improvements over traditional CNN architectures, achieving up to 79.95% accuracy in multitask cancer detection tasks, surpassing CNN-based models that achieved 74.05%. In liver lesion segmentation using CT scans, attention-enhanced U-Net models achieved a 93.4% Dice Similarity Coefficient (DSC) for liver tissue and 77.8% for tumor segmentation. In dermoscopy, self-supervised transformer-based models like G2LL exceeded 80% accuracy, while U-Net-based models for skin lesion segmentation achieved up to 93.32% accuracy. Histopathology image analysis further demonstrated that models incorporating attention mechanisms, such as the PistoSeg framework, improved segmentation precision by up to 7.15% compared to conventional methods. Across various modalities, Deep Learning models consistently outperform traditional methods, with improvements ranging from 5 to 15% in accuracy and segmentation metrics. Despite challenges such as computational demands and the need for large annotated datasets, Deep Learning continues to revolutionize medical image segmentation, offering higher diagnostic precision and outlining future research directions to bridge existing gaps.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".