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Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features
April 26, 2024, 4:46 a.m. | Youngmin Chung, Ji Hun Ha, Kyeong Chan Im, Joo Sang Lee
cs.CV updates on arXiv.org arxiv.org
Abstract: Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing cellular morphology at individual spots, the local context around these spots, and the global tissue organization. …
abstract analysis arxiv costs cs.cv deep learning deep learning framework features framework gene however limitations novel paper prediction predictive resolution robust spatial technology type
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