Explainable AI for accurate diagnosis of papillary thyroid carcinoma via fine-needle aspiration cytopathology

Abstract

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 (similar to 92% accuracy and similar to 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.