9 citations · 26 across the 26 of their papers we have counts for
26 papers
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 Pathology Foundation Model for Gastric Cancer with Real-World Validation
Ling Liang, Jiabo Ma, Zhengyu Zhang +25
Gastric cancer remains a major cause of cancer mortality, yet its histological and molecular heterogeneity complicates diagnosis and risk stratification. General-purpose pathology…
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 Breast Vision Pathology Foundation Model for Real-world Clinical Utility
Yingxue Xu, Zhengyu Zhang, Xiuming Zhang +32
Pathology foundation models have shown strong retrospective performance, but whether such systems can support clinically relevant use remains unclear. This challenge is particularl…
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23
Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objecti…
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…
Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment
Hongyi Wang, Zhengjie Zhu, Junlin Hou +13
The rapid digitization of histopathology slides has opened new opportunities for computational tools in clinical and research workflows. Content-based slide retrieval can help path…
A Clinical-grade Universal Foundation Model for Intraoperative Pathology
Zihan Zhao, Fengtao Zhou, Ronggang Li +20
Intraoperative pathology is pivotal to precision surgery, yet its clinical impact is constrained by diagnostic complexity and the limited availability of high-quality frozen-sectio…
Generative AI for Misalignment-Resistant Virtual Staining to Accelerate Histopathology Workflows
Jiabo MA, Wenqiang Li, Jinbang Li +7
Accurate histopathological diagnosis often requires multiple differently stained tissue sections, a process that is time-consuming, labor-intensive, and environmentally taxing due…
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 Foundation Model for AI-enabled Mammogram Interpretation
Fuxiang Huang, Jiayi Zhu, Yunfang Yu +20
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in women globally. Mammography is essential for the early detection and diagno…
A Unified Low-level Foundation Model for Enhancing Pathology Image Quality
Ziyi Liu, Zhe Xu, Jiabo Ma +7
Foundation models have revolutionized computational pathology by achieving remarkable success in high-level diagnostic tasks, yet the critical challenge of low-level image enhancem…
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…
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
Fang Yan, Jianfeng Wu, Jiawen Li +24
The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveragi…
FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition
Linshan Wu, Jiaxin Zhuang, Yanning Zhou +12
Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilit…
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
Hao Jiang, Cheng Jin, Huangjing Lin +15
Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening solution, current systems strug…
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
Zhengrui Guo, Conghao Xiong, Jiabo Ma +4
Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the sc…
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…
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Zhengrui Guo, Jiabo Ma, Yingxue Xu +3
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patie…
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni +18
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to…