16 citations · 21 across the 28 of their papers we have counts for
32 papers
Learning latent progression states from spatial heterogeneity in uterine histopathology
Qiming He, Yan Liu, Shuang Ge +20
Tumor progression is accompanied by changes in architecture, morphology and microenvironmental organization, yet progression-associated heterogeneity is usually compressed into sta…
DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis
Xiaoxiao Li, Xitong Ling, Jiawen Li +6
Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis. However, under long-tailed distributions, MIL-based WSI analysis faces a nes…
ConRub-Med: Reinforcement Learning with Consensus Rubrics for Open-Ended Medical Question Answering
Taojie Zhu, Yuan Xia, Tao Sun +8
Reinforcement learning with verifiable rewards has been especially effective in mathematics and coding, where answers can be checked automatically. Many open-ended medical question…
From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
Taojie Zhu, Wentao Zhao, Rui Sun +7
Evaluating whether large language model (LLM) agents can profit in capital markets is increasingly framed as end-to-end trading: place an agent in a historical market, let it trade…
Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
Weiming Chen, Xitong Ling, Zhenyang Cai +5
Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain shifts, and costly dense annot…
Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning
Taojie Zhu, Dongyang Xu, Ding Zou +4
Post-training paradigms for Large Language Models (LLMs), primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dilemma: SFT provides stability…
To What Extent Do Token-Level Representations from Pathology Foundation Models Improve Dense Prediction?
Weiming Chen, Xitong Ling, Xidong Wang +10
Pathology foundation models (PFMs) have rapidly advanced and are becoming a common backbone for downstream clinical tasks, offering strong transferability across tissues and instit…
HookMIL: Revisiting Context Modeling in Multiple Instance Learning for Computational Pathology
Xitong Ling, Minxi Ouyang, Xiaoxiao Li +7
Multiple Instance Learning (MIL) has enabled weakly supervised analysis of whole-slide images (WSIs) in computational pathology. However, traditional MIL approaches often lose cruc…
StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathology
Jiawen Li, Jiali Hu, Xitong Ling +6
Foundation models trained with self-supervised learning (SSL) on large-scale histological images have significantly accelerated the development of computational pathology. These mo…
From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology
Yizhi Wang, Li Chen, Qiang Huang +24
Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise,…
YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology
Deshui Yu, Yizhi Wang, Saihui Jin +9
Large language models (LLMs) excel on general tasks yet still hallucinate in high-barrier domains such as pathology. Prior work often relies on domain fine-tuning, which neither ex…
Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation
Mingxi Fu, Fanglei Fu, Xitong Ling +4
Pathological image segmentation faces numerous challenges, particularly due to ambiguous semantic boundaries and the high cost of pixel-level annotations. Although recent semi-supe…
Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis
Mingxi Fu, Xitong Ling, Yuxuan Chen +6
Accurate classification of Whole Slide Images (WSIs) and Regions of Interest (ROIs) is a fundamental challenge in computational pathology. While mainstream approaches often adopt M…
DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Minxi Ouyang, Lianghui Zhu, Yaqing Bao +10
Multimodal large models have shown great potential in automating pathology image analysis. However, current multimodal models for gastrointestinal pathology are constrained by both…
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Lianghui Zhu, Xitong Ling, Minxi Ouyang +24
Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological d…
A Simple Linear Patch Revives Layer-Pruned Large Language Models
Xinrui Chen, Haoli Bai, Tao Yuan +7
Layer pruning has emerged as a widely used technique for compressing large language models (LLMs). However, existing layer pruning approaches often incur substantial performance de…
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…
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology
Yuxuan Chen, Jiawen Li, Jiali Hu +4
With the rapid advancement of pathology foundation models (FMs), the representation learning of whole slide images (WSIs) attracts increasing attention. Existing studies develop hi…
Multimodal Distillation-Driven Ensemble Learning for Long-Tailed Histopathology Whole Slide Images Analysis
Xitong Ling, Yifeng Ping, Jiawen Li +8
Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI) datasets. The field of WSI anal…
Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
Jiawen Li, Jiali Hu, Qiehe Sun +6
The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. T…
Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis
Yuxuan Chen, Jiawen Li, Huijuan Shi +5
Bone metastasis analysis is a significant challenge in pathology and plays a critical role in determining patient quality of life and treatment strategies. The microenvironment and…
Unlocking adaptive digital pathology through dynamic feature learning
Jiawen Li, Tian Guan, Qingxin Xia +17
Foundation models have revolutionized the paradigm of digital pathology, as they leverage general-purpose features to emulate real-world pathological practices, enabling the quanti…
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
Xitong Ling, Yuanyuan Lei, Jiawen Li +5
Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images…
Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image
Jiawen Li, Qiehe Sun, Renao Yan +7
With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer d…