Dermoskopik Görüntü Segmentasyonunda Görsel Temel Modellerin Karşılaştırmalı Analizi ve Adaptasyon Stratejileri

Öz

Deri lezyonlarının doğru segmentasyonu, melanomun erken tanısında kritik öneme sahipken; Segment Anything Model (SAM) gibi büyük temel modellerin düşük kontrastlı ve belirsiz sınırlı tıbbi görüntülere doğrudan uygulanması yapısal alan farklılıkları nedeniyle yetersiz kalmaktadır. Bu çalışmada, dondurulmuş bir Vision Transformer omurgasına hafif adaptör modüllerinin entegre edildiği parametre verimli bir ince ayar stratejisi sunulmaktadır. Önerilen SAM-Adapter yöntemi; ISIC2018 ve HAM10000 açık erişimli dermoskopik veri setleri üzerinde orijinal SAM ve MedSAM modelleriyle karşılaştırmalı olarak değerlendirilmiştir. AdamW optimizasyonuyla eğitilen model; HAM10000 veri setinde en yüksek Dice (0.9603) ve IoU (0.9241) skorlarına ulaşırken, en düşük HD95 (22.57) değerini elde etmiştir. Wilcoxon testi ile istatistiksel anlamlılığı doğrulanan bu yaklaşım, hesaplama yükünü en aza indirerek zorlu lezyon sınırlarının belirlenmesinde üstün başarı göstermiştir. Geliştirilen adaptör tabanlı çerçevenin klinik karar destek sistemlerine ve farklı tıbbi görüntüleme modalitelerine entegrasyonu hedeflenmektedir.

Anahtar Kelimeler

• Deri Lezyonu Segmentasyonu
• Segment Anything Model
• SAM-Adapter
• Parametre Verimli İnce Ayar
• Tıbbi Görüntü Segmentasyonu

Comparative Analysis and Adaptation Strategies of Vision Foundation Models in Dermoscopic Image Segmentation

Öz

Accurate segmentation of skin lesions is of critical clinical importance for early melanoma diagnosis; however, the direct application of large foundation models like the Segment Anything Model (SAM) to medical images characterized by low contrast and fuzzy boundaries remains insufficient due to substantial domain gaps. In current study, a parameter-efficient fine-tuning strategy is presented by integrating lightweight adapter modules into a frozen Vision Transformer backbone. The proposed SAM-Adapter approach was comparatively evaluated against the original SAM and MedSAM models on the publicly available ISIC2018 and HAM10000 dermoscopic datasets. Optimized with AdamW, the model achieved the highest Dice (0.9603) and IoU (0.9241) scores along with the lowest HD95 (22.57) value on the HAM10000 dataset. Validated as statistically significant via the Wilcoxon test, this approach minimized computational overhead while demonstrating superior accuracy in boundary delineation. This adapter-based framework is planned for integration into clinical decision-support systems and other medical imaging modalities.

Anahtar Kelimeler

• Skin Lesion Segmentation
• Segment Anything Model
• SAM-Adapter
• Parameter-Efficient Fine-Tuning
• Medical Image Segmentation

Kaynakça

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