A Comparative Evaluation of Feature Aggregation Heads for Skin Lesion Classification Using Swin Transformer
12th International Conference of Image Processing, Wavelet and Applications on Real World Problems (IWW 2025), İstanbul, Turkey, 3 - 05 November 2025, pp.1-5, (Full Text)
- Publication Type: Conference Paper / Full Text
- City: İstanbul
- Country: Turkey
- Page Numbers: pp.1-5
- Kocaeli University Affiliated: Yes
Abstract
This paper presents a comparative analysis of feature aggregation heads for dermoscopic skin-lesion classification using a SwinV2 transformer. A reproducible pipeline is proposed to fine-tune a SwinV2 Tiny backbone on the HAM10000 benchmark, comparing three heads: a token mean baseline, a generalized mean pooling head that learns to emphasize informative tokens, and a stagewise multi-scale gated fusion head. All models were trained under a unified recipe with shared transforms and mild augmentations. Evaluation on a held-out test set, using accuracy, macro F1, macro ROC AUC, and macro average precision, shows the generalized mean head achieves the strongest class-balanced performance. The generalized mean head yielded a macro F1 of 0.779 and a macro ROC AUC of 0.980, while also attaining the highest accuracy of 0.873. The multi-scale head achieved an accuracy of 0.859 with competitive calibration. These results indicate that learned token aggregation, specifically the generalized mean head, improves ranking quality and minority class balance within a transparent and unified setup.