A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
Scanner-induced domain shifts undermine the robustness of pathology foundation models
3 Pith papers cite this work. Polarity classification is still indexing.
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Novel robustness losses added during downstream training on foundation-model features from pathology slides improve both robustness to technical variation and classification accuracy.
H-optimus-1 achieves the strongest externally validated survival prediction from histopathology images, with second-generation PFMs outperforming first-generation counterparts and a compact distilled model offering efficiency gains.
citing papers explorer
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Autonomous Drift Learning in Data Streams: A Unified Perspective
A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
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Enabling clinical use of foundation models for computational pathology
Novel robustness losses added during downstream training on foundation-model features from pathology slides improve both robustness to technical variation and classification accuracy.
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Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction
H-optimus-1 achieves the strongest externally validated survival prediction from histopathology images, with second-generation PFMs outperforming first-generation counterparts and a compact distilled model offering efficiency gains.