#5754. An ensemble deep learning for automatic prediction of papillary thyroid carcinoma using fine needle aspiration cytology

July 2026publication date
Proposal available till 11-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Engineering (all);
Computer Science Applications;
Artificial Intelligence;
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Abstract:
Accurately cytopathological diagnosis of Papillary Thyroid Carcinoma (PTC) is of importance for reducing costs and increasing efficiency of treatments. In this paper, we pursue that goal by introducing artificial intelligence (AI) for automatic classification of malignant PTC cell clusters from Fine Needle Aspiration Cytology (FNAC) processed by ThinPrep. High-resolution cytological images obtained with a 40 ? objective lens digital camera attached to an Olympus microscope were segmented into fragments and then divided into training, validation, and testing subsets. Fragments are non-overlapped patches containing only regions-of-interest that cover informative tissue structures for making proper diagnoses. Deep learning CNN models were pre-trained and fine-tuned on large-scale ImageNet domain before they were re-trained on cytology fragments. Moreover, we proposed a method to compute certainty of the patient-level prediction that undoubtedly provides additional evidence for reliability and confidence of the prediction.
Keywords:
Computer-aided diagnosis; Deep CNN models; Ensemble learning; Fine needle aspiration cytology; Papillary thyroid carcinoma; ThinPrep

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