Explainable AI for accurate diagnosis of papillary thyroid carcinoma via fine-needle aspiration cytopathology
Visual Computer, cilt.42, sa.9, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 42 Sayı: 9
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s00371-026-04558-z
- Dergi Adı: Visual Computer
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: Deep learning in cytopathology, Digital pathology, Explainable AI, Fine-needle aspiration cytology, Papillary thyroid carcinoma
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Erzincan Binali Yıldırım Üniversitesi Adresli: Evet
Özet
Papillary thyroid carcinoma (PTC) represents most thyroid cancer cases worldwide, making early and accurate diagnosis crucial for successful treatment. This study introduces an explainable AI framework for the automated analysis of fine-needle aspiration (FNA) cytopathology images. By integrating handcrafted features with deep learning-derived abstract features, the proposed model achieves high classification performance (~ 92% accuracy and ~ 96% AUC). The proposed architecture, incorporating multi-scale dilated and deformable convolutions, effectively captures irregularities in cell morphology. Visualizations demonstrate alignment with pathologists’ diagnostic criteria, enhancing the model’s clinical applicability and reliability. The code, dataset, and comprehensive documentation are publicly available via Zenodo (https://zenodo.org/records/18428647), ensuring full reproducibility of the experiments and results presented.