Fig. 2. Algorithm performance after fine-tuning.

Fine-tuned DeepDerm (A) and HAM10000 (B) on the DDI dataset (as described in Materials and Methods) compared to baseline (first three bars in each panel). Fine-tuning closes the gap between FST I–II and FST V–VI performance and leads to overall performance improvement. Ninety-five percent confidence interval is calculated using bootstrapping across the 20 seeds for both baseline and fine-tuned models to allow direct comparison.