AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
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.
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.
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