9 citations · 28 across the 24 of their papers we have counts for
25 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…
Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology
Jucheng Hu, Zhangquan Chen, Yulin Chen +9
Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical external signals (e.g., a cat's…
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 Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations
Andong Tan, Shuyu Dai, Jinglu Wang +7
Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasing…
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…
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 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…
Large-scale Self-supervised Video Foundation Model for Intelligent Surgery
Shu Yang, Fengtao Zhou, Leon Mayer +16
Computer-Assisted Intervention (CAI) has the potential to revolutionize modern surgery, with surgical scene understanding serving as a critical component in supporting decision-mak…
Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology
Wenhao Tang, Rong Qin, Heng Fang +4
Pre-trained encoders for offline feature extraction followed by multiple instance learning (MIL) aggregators have become the dominant paradigm in computational pathology (CPath), b…
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…
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
Andong Tan, Fengtao Zhou, Hao Chen
The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts, and then predicting the class l…
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…
Post-hoc Part-prototype Networks
Andong Tan, Fengtao Zhou, Hao Chen
Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal "where" a model looks at…
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
Huajun Zhou, Fengtao Zhou, Hao Chen
Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogene…