  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.
