Generation of Rule-Based Explanations of CNN Classifiers Using Regional Features
Por:
Philipp, W, Yashwanthika, R, Sikha, OK and Benítez R
Publicada:
5 sep 2024
Resumen:
Although Deep Learning networks generally outperform traditional machine learning approaches based on tailored features, they often lack explainability. To address this issue, numerous methods have been proposed, particularly for image-related tasks such as image classification or object segmentation. These methods generate a heatmap that visually explains the classification problem by identifying the most important regions for the classifier. However, these explanations remain purely visual. To overcome this limitation, we introduce a novel CNN explainability method that identifies the most relevant regions in an image and generates a decision tree based on meaningful regional features, providing a rule-based explanation of the classification model. We evaluated the proposed method on a synthetic blob's dataset and subsequently applied it to two cell image classification datasets with healthy and pathological patterns.
Filiaciones:
Philipp, W:
Univ Politecn Catalunya UPC BarcelonaTECH, Automat Control Dept, Barcelona 08034, Spain
Yashwanthika, R:
Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Comp, Coimbatore, India
Sikha, OK:
Univ Politecn Catalunya UPC BarcelonaTECH, Automat Control Dept, Barcelona 08034, Spain
Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Comp, Coimbatore, India
Benítez R:
Univ Politecn Catalunya UPC BarcelonaTECH, Automat Control Dept, Barcelona 08034, Spain
Inst Recerca Sant Joan de Deu IRSJD, Barcelona, Spain
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