Work

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

Year: 2026

Type: article

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 (~ 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.

Source: The Visual Computer

DOI: 10.1007/s00371-026-04558-z

Authors: Ahmet Kilicarslan, Canan Tastimur, Gokhan Gundogdu, Erhan Akin

Institutions: Erzincan Binali Yıldırım University, Fırat University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,190