Histopathological Analysis of Fine-Needle Aspiration Biopsies of Thyroid Nodules Using Explainable Convolutional Neural Networks
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2024
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LATIN AMERICAN CONGRESS ON BIOMEDICAL ENGINEERING, 9th; BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, 28th
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Resumo
Thyroid Cancer is a disease in which abnormal cells
grow uncontrollably in the gland with the potential to invade other
organs. Every year, almost 44,000 new cases are diagnosed worldwide.
Histopathological diagnosis of fine-needle aspiration biopsies of thyroid
nodules is the most precise exam to confirm the diagnosis and estimate
the stages of the disease. The diagnostic process in such an exam involves
detecting atypical signs, such as the presence of cell proliferation with
irregular shape and texture. This task could be even harder once you
consider that most thyroid biopsies might present multiple pathological
states, such as inflammatory diseases and hyperplasia. Therefore, this
paper addresses the development of a Computer Vision method to assist
the histopathological diagnosis of normal, thyroid papillary carcinoma
and goiter. The proposed method model and implement a Convolutional
Neural Network to detect visual patterns to differentiate the three pathological
states. Experiments following the Holdout Cross-Validation protocol
reached an accuracy of 88.73% for the multiclass approach and 95.74%
accuracy for the binary assessment. The results confirm the potential of
the proposed method to assist pathologists in prescribing a more precise
diagnosis.
Como referenciar
BAFFA, MATHEUS de F.O.; BACHMANN, LUCIANO; PEREIRA, THIAGO M.; ZEZELL, DENISE M.; SOARES, EDSON G.; PADUA, JOEL D.B.; FELIPE, JOAQUIM C. Histopathological Analysis of Fine-Needle Aspiration Biopsies of Thyroid Nodules Using Explainable Convolutional Neural Networks. In: LATIN AMERICAN CONGRESS ON BIOMEDICAL ENGINEERING, 9th; BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, 28th, October 24–28, 2022, Florianópolis, SC. Proceedings... Switzerland: Springer Nature Switzerland AG, 2024. p. 147–158. (IFMBE Proceedings, 90). DOI: 10.1007/978-3-031-49404-8_15. Disponível em: https://repositorio.ipen.br/handle/123456789/49565. Acesso em: 27 Mar 2026.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.