3 citations · 3 across the 9 of their papers we have counts for
11 papers
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…
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,…
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…
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…
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…
MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification
Mingxi Ouyang, Yuqiu Fu, Renao Yan +5
Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of…
Task-oriented Embedding Counts: Heuristic Clustering-driven Feature Fine-tuning for Whole Slide Image Classification
Xuenian Wang, Shanshan Shi, Renao Yan +4
In the field of whole slide image (WSI) classification, multiple instance learning (MIL) serves as a promising approach, commonly decoupled into feature extraction and aggregation.…