4 citations · 4 across the 5 of their papers we have counts for
5 papers
Cellular-Communication-Level Interpretability for Pathology Foundation Models via Graph Distillation on Microenvironment
Yuxiang Xiao, Zhiwei Chen, Dan Dai +5
Pathology foundation models (PFMs) provide strong tile-level representations but remain difficult to interpret at the cellular and microenvironmental scales that underpin clinical…
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…
Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer
Zhiwei Chen, Yang Hu, Yuxiang Xiao +7
Pathology foundation models (PFMs) provide strong tissue representations and have become central to digital pathology. However, deployment in disease-specific settings is limited b…
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…
When Large Language Model Meets Optimization
Sen Huang, Kaixiang Yang, Sheng Qi +1
Optimization algorithms and large language models (LLMs) enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, w…