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OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
2022
Proceedings of the 2022 International Conference on Multimedia Retrieval
Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in medical domain. While existing methods can be applied for class-wise retrieval (aka. inter-class), they cannot distinguish the granularity of likeness within the same class (aka. intra-class). The problem is exacerbated on medical external datasets, where noisy samples of the same class are treated equally during training. Our goal is to identify both intra/inter-class
doi:10.1145/3512527.3531425
fatcat:iiqp22hksfdttprkye2fophb5e