TissueFormer is introduced, a framework for pretraining foundation models with linear rather than quadratic computational complexity, overcoming a long-standing barrier to modeling long-range dependencies at scale.
Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, where signals from multiple cells are aggregated and critical cellular heterogeneity is obscured. Extending this paradigm to single-cell reso...
Zi-Jun Gao, Chun-Bin Gu, Jin-Xi Xiang et al.· 0 citations
Spatial transcriptomics (ST) enhances single-cell RNA sequencing by revealing transcript distribution, offering critical insights into heterogeneous diseases such as breast cancer. However, the high cost and lengthy processes of generating high-quality ST data limit clinical application. Recent deep learning methods pr...
It is argued that the most credible translational goal is not to infer every transcript from H&E, but to identify biologically coherent spatial states with reproducible morphological correlates that can be validated across independent platforms and cohorts before being mapped across large archival collections.
M. A. Rezania, Haechan Yi, Jae-Il Choi et al.· Cancer research and treatmen...· 0 citations
Spatial transcriptomics (ST) maps gene expression within intact tissues, but its cost and technical complexity limit broad adoption. A growing body of deep-learning methods now seeks to infer ST directly from routine histology images, especially hematoxylin and eosin (H&E)-stained slides, with the aim of turning archiv...
Ke-Xin Xu, H. Koohy, A. Antanaviciute· EXO· 0 citations
Thirty pathology-specific and general-purpose foundation models are benchmarked through 304,920 runs across spatial readouts, magnifications, taxonomic granularities, and evaluation protocols, yielding distinct multidimensional capability profiles.
Bo-Kai Zhao, Yi-Yang Zhang, Hanqing Chao et al.· 0 citations
Spatial omics technologies resolve molecular expression and spatial architecture at single-cell resolution, but profiling whole slides remains costly. In practice, only a few regions of interest (ROIs) are profiled, leaving the rest of the tissue unmeasured. S2-omics was the first framework to unify ROI selection with...
