8 papers
Spatial Message Passing in Language Space for Pathology Image Interpretation
Jing-Cheng Yang, Hao-Jung Wang, Jinhao Du +4
Multimodal Large Language Models (MLLMs) can generate pathological descriptions from histological images, but gigapixel Whole Slide Images (WSIs) exceed their visual context limits…
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…
Histology-informed tiling of whole tissue sections improves the interpretability and predictability of cancer relapse and genetic alterations
Willem Bonnaffé, Yang Hu, Andrea Chatrian +12
Histopathologists establish cancer grade by assessing histological structures, such as glands in prostate cancer. Yet, digital pathology pipelines often rely on grid-based tiling t…
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…
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…
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models
Willem Bonnaffé, CRUK ICGC Prostate Group, Freddie Hamdy +5
Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology.…