arXiv Machine Learning By Kritanu Chattopadhyay, Soumya Chatterjee, Ondrej Krejcar, Debotosh Bhattacharjee

HierarchicalDAEW: Domain-Aware Edge-Weighted Graph Convolution with Evidential Uncertainty for Multi-Section Spatial Gene Expression Prediction from H&E Histology

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arXiv:2607. 20896v1 Announce Type: new Abstract: Spatial transcriptomics assays remain costly and technically demanding, restricting transcriptome-wide profiling to specialist settings and preventing routine clinical deployment.

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arXiv Machine Learning
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arXiv Machine Learning
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Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy

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By Kai Standvoss, Miriam H\"agele, Rosemarie Krupar, Julika Ribbat-Idel, Jennifer Altsch\"uler, Gerrit Erdmann, Hans Pinckaers, Evelyn Ramberger, Madleen Drinkwitz, \'Ad\'am N\'arai, Alexander M\"ollers, Katja Lingelbach, Sebastian Kons, Lukas H\"onig, Recepcan Adig\"uzel, Joana Bai\~ao, Alberto Megina Gonzalo, Marius Teodorescu, Marie-Lisa Eich, Paolo Chetta, Shakil Merchant, Verena Aumiller, Simon Schallenberg, Andrew Norgan, Klaus-Robert M\"uller, Lukas Ruff, Maximilian Alber, Frederick Klauschen
arXiv Machine Learning
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INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer

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