Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features (2308.16483v2)
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.
- “Artificial intelligence and echocardiography,” Journal of Cardiovascular Imaging, vol. 29, no. 3, pp. 193, 2021.
- “Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy,” Circulation, vol. 138, no. 16, pp. 1623–1635, 2018.
- “Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the american society of echocardiography,” Journal of the American Society of Echocardiography, vol. 32, no. 1, pp. 1–64, 2019.
- “Clinically feasible and accurate view classification of echocardiographic images using deep learning,” Biomolecules, vol. 10, no. 5, pp. 665, 2020.
- “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” Advances in neural information processing systems, vol. 31, 2018.
- “Likelihood ratios for out-of-distribution detection,” Advances in neural information processing systems, vol. 32, 2019.
- “A simple fix to mahalanobis distance for improving near-ood detection,” arXiv preprint arXiv:2106.09022, 2021.
- “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826.
- “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8697–8710.
- “When does label smoothing help?,” Advances in neural information processing systems, vol. 32, 2019.
- “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
- AI-Hub, ,” https://aihub.or.kr/aihubdata/data/view.do ?currMenu=115&topMenu=&aihubDataSe=data&dataSetSn =502.
- “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning. PMLR, 2019, pp. 6105–6114.
- “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
- “Sgdr: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983, 2016.
- “Randaugment: Practical automated data augmentation with a reduced search space,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020, pp. 702–703.
- “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” arXiv preprint arXiv:1610.02136, 2016.