Deep neural networks can differentiate thyroid pathologies on infrared hyperspectral images.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 24, 2024

Abstract

BACKGROUND AND OBJECTIVE: The thyroid is a gland responsible for producing important body hormones. Several pathologies can affect this gland, such as thyroiditis, hypothyroidism, and thyroid cancer. The visual histological analysis of thyroid specimens is a valuable process that enables pathologists to detect diseases with high efficiency, providing the patient with a better prognosis. Existing computer vision systems developed to aid in the analysis of histological samples have limitations in distinguishing pathologies with similar characteristics or samples containing multiple diseases. To overcome this challenge, hyperspectral images are being studied to represent biological samples based on their molecular interaction with light.

Authors

  • Matheus de Freitas Oliveira Baffa
    Department of Computing and Mathematics, University of São Paulo, Bandeirantes Av. 3900, Monte Alegre, Ribeirão Preto, SP 14040-901, Brazil.
  • Denise Maria Zezell
    Nuclear and Energy Research Institute, São Paulo, SP, Brazil.
  • Luciano Bachmann
    Department of Physics, University of São Paulo, Ribeirão Preto, SP, Brazil.
  • Thiago Martini Pereira
    Department of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil.
  • Thomas Martin Deserno
    Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig, Braunschweig, Germany.
  • Joaquim Cezar Felipe
    Department of Computing and Mathematics, University of São Paulo at Ribeirão Preto, 14040-901 Ribeirão Preto, SP, Brazil. Electronic address: jfelipe@ffclrp.usp.br.

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