Aprendizado profundo para assistência histopatológica: um estudo de classificação de micrometástases em câncer de mama

• Gabriela Kuhn UNISINOS
• Felipe André Zeiser UNISINOS
• Adriana Roehe UFCSPA
• Cristiano André da Costa UNISINOS
• Gabriel de Oliveira Ramos UNISINOS

DOI: https://doi.org/10.5753/sbcas.2023.230093

Abstract

Deep Learning algorithms for detecting micrometastasis in breast cancer can improve the gold standards and the efficiency of pathologists’ routines. An analysis of the literature indicates that the models still need to improve their performance in identifying micrometastases and tumor cells. In the case of isolated tumor cells, the detection rates were below 40%. There are also opportunities to improve on positive rates, since many models still detect nerves or contaminations as false micrometastases. In this study, we investigated the implementation to perform the classifications of metastasis. The goal is that this classification neural network become the first layer of a two-layer final neural network model - classification and segmentation. As partial results, this work achieved an AUC = 0.998 for the classification task on the slide patch.

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