3 papers
cs.AI2026
ReCBM: Uncertainty-Gated Relational Reasoning for Concept Bottleneck Models
An Sui, Yuzhu Li, Fuping Wu +1
Concept Bottleneck Models (CBMs) provide an interpretable framework by grounding predictions in human-understandable concepts, enabling semantic inspection and test-time interventi…
cs.CV2026
Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation
An Sui, Yuzhu Li, Gunter Schumann +2
Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as medical image segmentation.…
cs.CV2025
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
Yuzhu Li, An Sui, Fuping Wu +1
Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability, particularly in deep learning approaches. However, previous methods g…