9 citations · 38 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
A Deployment-Friendly Foundational Framework for Efficient Computational Pathology
Yu Cai, Cheng Jin, Zhengyu Zhang +25
Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis. Here, we present LitePath, a dep…
MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Image
Qian Zeng, Yihui Wang, Shu Yang +9
Whole-slide images (WSIs) are an important data modality in computational pathology, yet their gigapixel resolution and lack of fine-grained annotations challenge conventional deep…
LLM-driven Knowledge Enhancement for Multimodal Cancer Survival Prediction
Chenyu Zhao, Yingxue Xu, Fengtao Zhou +2
Current multimodal survival prediction methods typically rely on pathology images (WSIs) and genomic data, both of which are high-dimensional and redundant, making it difficult to…
GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction
Jiarui Ouyang, Yihui Wang, Yihang Gao +3
Spatial Transcriptomics (ST) offers spatially resolved gene expression but remains costly. Predicting expression directly from widely available Hematoxylin and Eosin (H&E) stained…
Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images
Cheng Jin, Fengtao Zhou, Yunfang Yu +13
Precision oncology requires accurate molecular insights, yet obtaining these directly from genomics is costly and time-consuming for broad clinical use. Predicting complex molecula…
PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
Jiabo Ma, Yingxue Xu, Fengtao Zhou +23
The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer d…
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
Yingxue Xu, Yihui Wang, Fengtao Zhou +16
Remarkable strides in computational pathology have been made in the task-agnostic foundation model that advances the performance of a wide array of downstream clinical tasks. Despi…
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Sunan He, Yuxiang Nie, Hongmei Wang +21
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
Yilan Zhang, Yingxue Xu, Jianqi Chen +2
Multimodal learning significantly benefits cancer survival prediction, especially the integration of pathological images and genomic data. Despite advantages of multimodal learning…