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Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search. (arXiv:2208.13653v2 [cs.CV] UPDATED)
Sept. 26, 2022, 1:14 a.m. | Sobhan Hemati, Shivam Kalra, Morteza Babaie, H.R. Tizhoosh
cs.CV updates on arXiv.org arxiv.org
Learning suitable Whole slide images (WSIs) representations for efficient
retrieval systems is a non-trivial task. The WSI embeddings obtained from
current methods are in Euclidean space not ideal for efficient WSI retrieval.
Furthermore, most of the current methods require high GPU memory due to the
simultaneous processing of multiple sets of patches. To address these
challenges, we propose a novel framework for learning binary and sparse WSI
representations utilizing a deep generative modelling and the Fisher Vector. We
introduce new …
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