… of breast cancer diagnosis with the use of interpretability methods. The paper aims to: (i)
introduce the use of interpretability … understand the underlying mechanisms of breast cancer, (ii) …

… We provide the diagnosis in an interpretable way through segmentation and heat maps. …
In order to enhance diagnosis interpretability, we can combine semantic segmentation …

… offer a definitive diagnosis and interpretability is therefore … with CNN models offers
interpretable decision making. The … To address this challenge, we introduce a novel interpretable …

… , autonomous vehicle navigation, etc, interpretability is more … with interpretability for the
diagnosis of breast cancer. … An important aspect of this work is the interpretability of the results …

… In this section, we devise an interpretable diagnosis system for breast cancer detection. To
be specific, an improved random forest-based rule extraction (IRFRE) method is developed to …

… In this context, interpretability is used to help domain experts learn new patterns and … on
the Wisconsin Original dataset for breast cancer diagnosis. Results showed that local LIME …

… We identified 600 consecutive female patients, from 2012 to 2019, with invasive breast
cancer diagnosis and available preoperative mammography and US at our institution. The …

… We propose a novel MA-MIDN model for interpretable breast cancer histopathological
image diagnosis. The model seamlessly integrates MINN, simple CNN-based feature extractor, …

… This research aims to tackle the black box problem in breast cancer diagnostics in the
healthcare field. Although several studies have achieved substantial advancements in improving …

… on BI-RADS in breast cancer diagnosis. To effectively learn … generative network and an
interpretable diagnosis network. To … effectively provide interpretation of diagnosis as well as mass …