Algorithm 1: Source-Only Group-Aware Multimodal Training

Input:   HAM10000 source domain D1   Derm7pt source domain D2   Harmonized class set Y   Model parameters Θ    E

Output:     Source-validated model Θ*

Method: 1: Harmonize diagnostic labels and metadata across D1 and D2 2: Construct patient-level train, validation, calibration, threshold-fitting, and internal-test partitions 3: Initialize model parameters Θ 4: for epoch = 1 to E do 5: Construct domain-balanced and group-aware mini-batches 6: for each mini-batch B do 7: Extract multi-scale visual features 8: Generate transformer contextual representation zv 9: Encode metadata and availability indicators zm 10: Compute gated multimodal representation z 11: Compute class probabilities and focal loss Lcls 12: Compute the mean focal loss Rg for each sufficiently represented supported group. 13: Compute the smooth worst-group robustness term Lgroup with τ = 0.10. 14: Compute Ltrain = Lcls + 0.10Lgroup. 15: Update model parameters using AdamW with weight decay 1 × 10−4. 16: end for 17: Evaluate macro-F1, worst-group F1, and validation loss 18: Apply early stopping using source validation only 19: end for 20: Return the best source-validated model Θ*