9 citations · 38 across the 18 of their papers we have counts for
19 papers
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…
Democratizing and accelerating AI-driven pathology research through agentic intelligence
Jiabo Ma, Cheng Jin, Yihui Wang +19
Computational pathology has advanced rapidly with the emergence of foundation models, yet widespread adoption remains limited by substantial technical complexity and programming re…
A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning
Zhe Xu, Zhengyu Zhang, Zhiyuan Cai +26
Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to…
Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model
Fengtao Zhou, Yingxue Xu, Zhengyu Zhang +20
Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times. While…
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…
A Multimodal Foundation Model to Enhance Generalizability and Data Efficiency for Pan-cancer Prognosis Prediction
Huajun Zhou, Fengtao Zhou, Jiabo Ma +6
Multimodal data provides heterogeneous information for a holistic understanding of the tumor microenvironment. However, existing AI models often struggle to harness the rich inform…
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Zhe Xu, Ziyi Liu, Junlin Hou +13
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with lang…
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…
Distilled Prompt Learning for Incomplete Multimodal Survival Prediction
Yingxue Xu, Fengtao Zhou, Chenyu Zhao +3
The integration of multimodal data including pathology images and gene profiles is widely applied in precise survival prediction. Despite recent advances in multimodal survival mod…
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
Jiabo Ma, Zhengrui Guo, Fengtao Zhou +20
Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial fo…
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…
Multimodal Data Integration for Precision Oncology: Challenges and Future Directions
Huajun Zhou, Fengtao Zhou, Chenyu Zhao +3
The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The i…
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…
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images
Zhikang Wang, Yumeng Zhang, Yingxue Xu +3
Histo-genomic multi-modal methods have recently emerged as a powerful paradigm, demonstrating significant potential for improving cancer prognosis. However, genome sequencing, unli…
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…