6 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…
TraceTrans: Translation and Spatial Tracing for Surgical Prediction
Xiyu Luo, Haodong Li, Xinxing Cheng +4
Image-to-image translation models have achieved notable success in converting images across visual domains and are increasingly used for medical tasks such as predicting post-opera…
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…
Structure Unbiased Adversarial Model for Medical Image Segmentation
Tianyang Zhang, Shaoming Zheng, Jun Cheng +7
Generative models have been widely proposed in image recognition to generate more images where the distribution is similar to that of the real ones. It often introduces a discrimin…