GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions

Mehdi Yousefzadeh; Parsa Esfahanian; Sara Rashidifar; Hossein Salahshoor Gavalan; Negar Sadat Rafiee Tabatabaee; Saeid Gorgin; Dara Rahmati; Maryam Daneshpazhooh

arXiv · 2509.12277

GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions

Mehdi Yousefzadeh·Parsa Esfahanian·Sara Rashidifar·Hossein Salahshoor Gavalan·Negar Sadat Rafiee Tabatabaee·Saeid Gorgin·Dara Rahmati·Maryam Daneshpazhooh

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Abstract

Introduction. Dermoscopy aids melanoma triage, yet image-only AI often ignores patient metadata (age, sex, site) and the physical scale needed for geometric analysis. We present GraphDerm, a population-graph framework that fuses imaging, millimeter-scale calibration, and metadata for multiclass dermoscopic classification, to the best of our knowledge the first ISIC-scale application of GNNs to dermoscopy. Methods. We curate ISIC 2018/2019, synthesize ruler-embedded images with exact masks, and train U-Nets (SE-ResNet-18) for lesion and ruler segmentation. Pixels-per-millimeter are regressed from the ruler-mask two-point correlation via a lightweight 1D-CNN. From lesion masks we compute real-scale descriptors (area, perimeter, radius of gyration). Node features use EfficientNet-B3; edges encode metadata/geometry similarity (fully weighted or thresholded). A spectral GNN performs semi-supervised node classification; an image-only ANN is the baseline. Results. Ruler and lesion segmentation reach Dice 0.904 and 0.908; scale regression attains MAE 1.5 px (RMSE 6.6). The graph attains AUC 0.9812, with a thresholded variant using about 25% of edges preserving AUC 0.9788 (vs. 0.9440 for the image-only baseline); per-class AUCs typically fall in the 0.97-0.99 range. Conclusion. Unifying calibrated scale, lesion geometry, and metadata in a population graph yields substantial gains over image-only pipelines on ISIC-2019. Sparser graphs retain near-optimal accuracy, suggesting efficient deployment. Scale-aware, graph-based AI is a promising direction for dermoscopic decision support; future work will refine learned edge semantics and evaluate on broader curated benchmarks.

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Mehdi Yousefzadeh, Parsa Esfahanian, Sara Rashidifar, Hossein Salahshoor Gavalan, Negar Sadat Rafiee Tabatabaee, Saeid Gorgin, Dara Rahmati, Maryam Daneshpazhooh. 2025-09-14. GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions. https://arxiv.org/abs/2509.12277

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