Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from routinely available hematoxylin and eosin (HE) images therefore offers a scalable alternative. However, conventional methods often fit high-dim...
Shi-Ting Ruan, Xi-Tong Ling, Qi-Ming He et al.· 0 citations
DeCo-MIL is proposed for long-tailed WSI analysis, which jointly alleviates the nested dual long-tail through frequency-debiased counterfactual reasoning and achieves state-of-the-art performance in both tail-class recognition and overall classification.
Xiao-Xiao Li, Xi-Tong Ling, Jiawen Li et al.· 0 citations
A distillation-based pretraining framework for MIL is proposed, which leverages two slide-level foundation models, TITAN and CARE, as teachers to transfer their representational knowledge into a diverse set of MIL architectures.
Ming-Xi Fu, Jiawen Li, Renao Yan et al.· arXiv.org· 0 citations
This work presents ALICE, a unified foundation model trained through multi-stage agglomerative distillation that sequentially distills eight vision-only, vision-language, and slide-level teacher models into dedicated modules of a single backbone for broad computational pathology applications.
Jiawen Li, Tian Guan, Huijuan Shi et al.· arXiv.org· 0 citations
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