![]() Similarity learning paradigm, Geometric Visual Similarity Learning, whichĮmbeds the prior of topological invariance into the measurement of the Problem of this task, i.e., learning a consistent representation between imagesįor a clustering effect of same semantic features. ![]() ![]() Of consistent representation for same semantics. Semantic-independent variation in 3D medical images make it challenging to getĪ reliable measurement for the inter-image similarity, hindering the learning However, the lack of the semantic prior in metrics and the Self-supervised pre-training, due to their sharing of numerous same semantic Authors: Yuting He, Guanyu Yang, Rongjun Ge, Yang Chen, Jean-Louis Coatrieux, Boyu Wang, Shuo Li Download PDF Abstract: Learning inter-image similarity is crucial for 3D medical images
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