16 citations · 20 across the 24 of their papers we have counts for
28 papers
SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
Shiting Ruan, Xitong Ling, Qiming He +6
Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from rou…
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…
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
Mingxi Fu, Jiawen Li, Renao Yan +4
Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology. However, existing MIL aggregators are still typically…
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
Jiawen Li, Tian Guan, Huijuan Shi +5
Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary…
SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
Mingyi He, Xinyi Guo, Xitong Ling +7
Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous. This…
GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights
Qiming He, Jing Li, Tian Guan +26
Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging…
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…
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…
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
Xitong Ling, Minxi Ouyang, Yizhi Wang +8
Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist path…