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cs.CV2026
Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion
Yuxiang Xiao, Yang Hu, Bin Li +5
Pathology foundation models (PFMs) provide strong tile-level representations via self-supervised pre-training on large-scale pathology images. Yet, PFMs are developed under diverse…
cs.CV2026
Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors
Ziang Xu, Bin Li, Yang Hu +4
Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reliable depth and pose estimation. Howeve…
cs.CV2025
AdaFusion: Prompt-Guided Inference with Adaptive Fusion of Pathology Foundation Models
Yuxiang Xiao, Yang Hu, Bin Li +5
Pathology foundation models (PFMs) have demonstrated strong representational capabilities through self-supervised pre-training on large-scale, unannotated histopathology image data…