6 citations · 8 across the 4 of their papers we have counts for
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cs.CV2024★ 6 cited
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
Junlin Hou, Sicen Liu, Yequan Bie +4
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable…
cs.CV2024
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
Andong Tan, Fengtao Zhou, Hao Chen
The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts, and then predicting the class l…
cs.CV2024★ 1 cited
Post-hoc Part-prototype Networks
Andong Tan, Fengtao Zhou, Hao Chen
Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal "where" a model looks at…