Fine-Grained Concept Bottleneck Models Enhance Interpretability in Medical Imaging
A recent study introduces a detailed Concept Bottleneck Model (CBM) framework that connects each concept to specific visual evidence, allowing for direct examination of the encoding process. This approach enables users to understand predictions and confirm that the model captures the intended concepts instead of misleading correlations. Testing on medical imaging benchmarks indicates that the developed concept space is fully informative and achieves predictive accuracy similar to traditional CBMs, while greatly enhancing transparency. In contrast to post-hoc attribution techniques, this framework ensures both the existence and accuracy of concept representations, merging interpretability with reliability. The research can be found on arXiv titled 'Towards Fine-Grained and Verifiable Concept Bottleneck Models' (arXiv:2605.14210).
Key facts
- The paper proposes a fine-grained CBM framework that grounds concepts in localized visual evidence.
- The framework enables direct inspection of where and how concepts are encoded.
- Users can verify that the model learns intended concepts rather than spurious correlations.
- Experiments were conducted on medical imaging benchmarks.
- The learned concept space is information-complete.
- Predictive performance is comparable to standard CBMs.
- The framework substantially improves transparency.
- It validates both the presence and correctness of concept representations.
Entities
Institutions
- arXiv