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PathoSpotter: Computational Intelligence Applied to Nephropathology

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Innovations in Nephrology

Abstract

Evidence-based medicine has received increasing attention. This type of medicine would have the benefit of using large data sets to investigate clinical–laboratory associations and validate hypotheses grounded on data. Pathology is one area that has been benefited from large data sets of images, having advances leveraged by computational pathology, which in turn relies in the advances of the methods conceived by the computational intelligence and the computer vision fields. This type of medicine would benefit of using large. By particularly considering kidney biopsies, computational nephropathology seeks to identify renal lesions from primary computer vision tasks that involve classification and segmentation of renal structures on histology images. In this context, this chapter aims at discussing some advances in computational nephropathology, contextualizing them in the scope of the PathoSpotter project. We also address current achievements and challenges, as well as dig in future prospects to the field.

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Notes

  1. 1.

    https://pathospotter.bahia.fiocruz.br

  2. 2.

    The Brazilian Health Ministry Research Agency. https://portal.fiocruz.br/en/

  3. 3.

    https://pathospotter.bahia.fiocruz.br/pathospottersearch

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Acknowledgments

The PathoSpotter project is partially sponsored by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB), grants TO-P0008/15 and TO-SUS0031/2018, and by the Inova FIOCRUZ grant. Washington dos Santos and Luciano Oliveira are research fellows of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grants 306779/2017 and 307550/2018-4, respectively.

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Authors and Affiliations

  1. Intelligent Vision Research Lab, Institute of Computing, Federal University of Bahia, Salvador, Brazil

    Luciano Oliveira & Paulo Chagas

  2. High Performance Computing Laboratory, Department of Technology, University of Feira de Santana, Feira de Santana, Brazil

    Angelo Duarte

  3. Advanced Data Analysis and Management Lab, Department of Exact Sciences, University of Feira de Santana, Feira de Santana, Brazil

    Rodrigo Calumby

  4. Department of Computer Science, Federal University of Ceará, Fortaleza, Brazil

    Emanuele Santos

  5. Departament of Exact Sciences, University of Feira de Santana, Feira de Santana, Brazil

    Michele Angelo

  6. Oswaldo Cruz Foundation, Gonçalo Moniz Institute, Salvador, Brazil

    Washington dos Santos

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  1. Luciano Oliveira
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  2. Paulo Chagas
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  5. Emanuele Santos
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  7. Washington dos Santos
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Correspondence to Luciano Oliveira .

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Editors and Affiliations

  1. School of Medicine, University of Fortaleza, Fortaleza, Ceará, Brazil

    Geraldo Bezerra da Silva Junior

  2. Division of Nephrology and Endocrinology, University of Tokyo School of Medicine, Tokyo, Japan

    Masaomi Nangaku

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Oliveira, L. et al. (2022). PathoSpotter: Computational Intelligence Applied to Nephropathology. In: Bezerra da Silva Junior, G., Nangaku, M. (eds) Innovations in Nephrology. Springer, Cham. https://doi.org/10.1007/978-3-031-11570-7_16

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