10 citations · 19 across the 17 of their papers we have counts for
17 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…
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…
LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning
Haoxuan Che, Haibo Jin, Zhengrui Guo +3
LLMs have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scat…
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…
Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining
Qichen Sun, Zhengrui Guo, Rui Peng +2
Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.…
Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification
Conghao Xiong, Zhengrui Guo, Zhe Xu +6
Few-shot Whole Slide Image (WSI) classification is severely hampered by overfitting. We argue that this is not merely a data-scarcity issue but a fundamentally geometric problem. G…
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning
Wei Wu, Can Liao, Zizhen Deng +2
The Platonic Representation Hypothesis suggests a universal, modality-independent reality representation behind different data modalities. Inspired by this, we view each neuron as…
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…
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation
Zhengrui Guo, Fangxu Zhou, Wei Wu +4
Modeling the nonlinear dynamics of neuronal populations represents a key pursuit in computational neuroscience. Recent research has increasingly focused on jointly modeling neural…
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…
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…
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Cheng Jin, Zhengrui Guo, Yi Lin +2
Deep learning has significantly advanced medical imaging analysis (MIA), achieving state-of-the-art performance across diverse clinical tasks. However, its success largely depends…