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arXiv · 2308.16483

Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features

Jaeik Jeon·Seongmin Ha·Yeonggul Jang·Yeonyee E. Yoon·Jiyeon Kim·Hyunseok Jeong·Dawun Jeong·Youngtaek Hong·Seung-Ah Lee Hyuk-Jae Chang
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Abstract

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.

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Jaeik Jeon, Seongmin Ha, Yeonggul Jang, Yeonyee E. Yoon, Jiyeon Kim, Hyunseok Jeong, Dawun Jeong, Youngtaek Hong, Seung-Ah Lee Hyuk-Jae Chang. 2023-08-31. Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features. https://arxiv.org/abs/2308.16483

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