A Study on Class Activation Map Methods to Detect Masses in Mammography Images using Weakly Supervised Learning

  • Vicente Sampaio UFRPE
  • Filipe R. Cordeiro UFRPE

Resumo


Nos últimos anos, modelos de aprendizado fracamente supervisionado têm auxiliado na detecção de lesões em imagens de mamografia, diminuindo a necessidade de anotações de pixels na imagem. A maioria dos modelos na literatura se baseia no uso de mapas de ativação CAM, não sendo explorado o uso de outros modelos de ativação para detecção em imagens de mamografia. Este trabalho apresenta um estudo do uso de diferentes métodos de mapas de ativação do estado da arte, aplicados para treinamento fracamente supervisionado em imagens de mamografia. Neste estudo, comparamos os métodos CAM, GradCAM, GradCAM++, XGradCAM e LayerCAM, utilizando a rede GMIC para detectar a presença de lesões em imagens de mamografia. A avaliação é feita utilizando a base VinDr-Mammo, utilizando as métricas de Acurácia, TPR, FNR e FPPI. Resultados mostram que o uso de diferentes estratégias de mapas de ativação nas etapas de treino e teste melhoram o resultado do modelo. Com isso, melhoramos os resultados do método GMIC, reduzindo o valor de FPPI e aumentando o valor de TPR.

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Publicado
28/11/2022
SAMPAIO, Vicente; CORDEIRO, Filipe R.. A Study on Class Activation Map Methods to Detect Masses in Mammography Images using Weakly Supervised Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 437-448. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2022.227318.

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