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Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
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 Artificial Intelligence (XAI).
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
To the best of our knowledge, our "OpenCBM" is the first CBM with concepts of open vocabularies, providing users the unique benefit such as removing, adding, or replacing any desired concept to explain the model's prediction even after a model is trained.
Post-hoc Part-prototype Networks
Therefore, a natural question is: can one construct a network that answers both "where" and "what" in a post-hoc manner to guarantee the model's performance?
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020.
Explicitly Modeled Attention Maps for Image Classification
Self-attention networks have shown remarkable progress in computer vision tasks such as image classification.
