Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features
This work addresses a critical problem for medical imaging practitioners by improving OOD detection in echocardiography, though it appears incremental as it builds on existing methods like Mahalanobis distance.
The paper tackled the challenge of detecting out-of-distribution (OOD) data in echocardiographic view classification, particularly for near-OOD scenarios with subtle differences, by introducing label smoothing to enhance semantic features and combining it with Mahalanobis distance, establishing a new benchmark for accuracy.
In echocardiographic view classification, accurately detecting out-of-distribution (OOD) data is essential but challenging, especially given the subtle differences between in-distribution and OOD data. While conventional OOD detection methods, such as Mahalanobis distance (MD) are effective in far-OOD scenarios with clear distinctions between distributions, they struggle to discern the less obvious variations characteristic of echocardiographic data. In this study, we introduce a novel use of label smoothing to enhance semantic feature representation in echocardiographic images, demonstrating that these enriched semantic features are key for significantly improving near-OOD instance detection. By combining label smoothing with MD-based OOD detection, we establish a new benchmark for accuracy in echocardiographic OOD detection.