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Breast Cancer Histologic Grade Identification by Graph Neural Network Embeddings

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Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13920))

Abstract

Deep neural networks are nowadays state-of-the-art methodologies for general-purpose image classification. As a consequence, such approaches are also employed in the context of histopathology biopsy image classification. This specific task is usually performed by separating the image into patches, giving them as input to the Deep Model and evaluating the single sub-part outputs. This approach has the main drawback of not considering the global structure of the input image and can lead to avoiding the discovery of relevant patterns among non-overlapping patches. Differently from this commonly adopted assumption, in this paper, we propose to face the problem by representing the input into a proper embedding resulting from a graph representation built from the tissue regions of the image. This graph representation is capable of maintaining the image structure and considering the relations among its relevant parts. The effectiveness of this representation is shown in the case of automatic tumor grading identification of breast cancer, using public available datasets.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Palermo, 90123, Palermo, Italy

    Salvatore Calderaro & Giosué Lo Bosco

  2. Institute for High-Performance Computing and Networking, National Research Council of Italy, 90146, Palermo, Italy

    Filippo Vella & Riccardo Rizzo

Authors
  1. Salvatore Calderaro
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  2. Giosué Lo Bosco
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  3. Filippo Vella
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  4. Riccardo Rizzo
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Corresponding author

Correspondence to Salvatore Calderaro .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas Ruiz

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Calderaro, S., Bosco, G.L., Vella, F., Rizzo, R. (2023). Breast Cancer Histologic Grade Identification by Graph Neural Network Embeddings. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-34960-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34959-1

  • Online ISBN: 978-3-031-34960-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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