Generation of Rule-Based Explanations of CNN Classifiers Using Regional Features
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 segmentatio...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Fundació Sant Joan de Déu |
| Repositorio: | r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu |
| OAI Identifier: | oai:fsjd.fundanetsuite.com:p26857 |
| Acceso en línea: | https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=26857 |
| Access Level: | acceso abierto |
| Palabra clave: | Explainable AI Decision tree CNN interpretability Rule based explanations |
| Sumario: | 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. |
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