Leveraging Image-to-Text Generators in Multimodal Vision Transformers for Inclusive Skin Cancer Diagnosis: A Comparative Study
Bibliographic record
Abstract
AI models for skin cancer diagnosis often underperform on darker skin tones due to imbalanced training datasets that predominantly feature lighter skin. In this study, we investigate whether lightweight, textual input can mitigate this disparity in a low-data setting. We use a dataset of only 4,311 clinical dermatology images—3,900 from lighter skin tones and just 411 from darker tones—to train Vision Transformers (ViTs) enhanced with text input including skin tone and generated lesion descriptions from Gemini and MONET. These textual inputs are fused with visual features via late fusion strategies. Among all configurations, ViT-B/32 combined with BERT-encoded skin tone using Element-Wise Fusion achieved the most balanced results, with AUCs of 0.822 (light) and 0.825 (dark), and matched accuracies of 0.823. This setup reduced the AUC gap to 0.003 and the accuracy gap to 0.0001. Our findings show that incorporating simple and domain-specific textual input can substantially reduce skin tone bias in ViT-based diagnosis offering a practical solution for building fairer medical AI.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".