An interpretable graph neural network for histological image analysis
The rapid advancements in machine learning have opened up new possibilities for solving complex problems, including in highly specialized applications with notable impact on relevant diseases, such as cancer. A critical aspect of cancer treatment is histopathological image analysis, a field that ana...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | Brasil |
| Institución: | Universidade de São Paulo (USP) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da USP |
| Idioma: | inglés |
| OAI Identifier: | oai:teses.usp.br:tde-11082025-101431 |
| Acceso en línea: | https://www.teses.usp.br/teses/disponiveis/55/55134/tde-11082025-101431/ |
| Access Level: | acceso abierto |
| Palabra clave: | Graph Neural Network Histopathology Histopatologia Rede Neural em Grafos Whole Slide Image |
| Sumario: | The rapid advancements in machine learning have opened up new possibilities for solving complex problems, including in highly specialized applications with notable impact on relevant diseases, such as cancer. A critical aspect of cancer treatment is histopathological image analysis, a field that analyzes large zoomable images at a microscope level, to understand how the disease affected the patient, enabling patient-tailored treatment strategies. However, progress in this area relies heavily on interdisciplinary collaboration and the development of methods that utilize highquality annotated datasets effectively. In this thesis, we address these challenges by proposing a new collaborative annotation software for Whole Slides Images. Using the software, we aid in the construction of novel annotated datasets with application-specific goals. Then, inspired by the pathologist\'s work methodology, we develop a novel Graph Neural Network (GNN) technique based on activation maps that models neighborhood representation based on the presence or absence of features in the neighborhood. Our approach is based on the Message Passing architecture, which although very popular, may limit graph representation by suffering from issues such as limited injectability and oversquashing, depending on the GNN architecture and task. Moreover, increasing traditional GNN\'s receptive field requires deepening the neural network, which introduces additional challenges, such as higher computational requirements. In contrast, our approach projects the graph\'s features to a fixed-size map representation with Self-Organizing Maps, which are activated based on the presence and distance of features from the central node, within the model\'s configurable receptive field. Lastly, the activation map keeps the graph\'s features within their original domain during aggregation, which has benefits for interpretability. We conclude this work by training the proposed GNN on a novel annotated dataset developed with the proposed tool, and using its interpretability advantages to highlight the patterns it identifies, contrasting it with a pathologist\'s expectations. This work contributes to the advancement of histopathological image analysis by proposing novel research tools, annotated data, and methods, paving the way for more accurate and reliable AI-based personalized medicine. |
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