4 citations · 11 across the 15 of their papers we have counts for
15 papers
Agentic Visual Reasoning in Whole-Slide Pathology Images via Active Perception
Jingyun Chen, Fengchun Liu, Linghan Cai +5
Whole-slide visual reasoning requires identifying sparse diagnostic evidence in gigapixel pathology slides and integrating observations across spatial scales. Existing WSI methods…
PathReasoner-R1: Instilling Structured Reasoning into Pathology Vision-Language Model via Knowledge-Guided Policy Optimization
Songhan Jiang, Fengchun Liu, Ziyue Wang +2
Vision-Language Models (VLMs) are advancing computational pathology with superior visual understanding capabilities. However, current systems often reduce diagnosis to directly out…
PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology
Fengchun Liu, Songhan Jiang, Linghan Cai +2
While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) co…
PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning
Jingyun Chen, Linghan Cai, Zhikang Wang +5
Analyzing whole-slide images (WSIs) requires an iterative, evidence-driven reasoning process that parallels how pathologists dynamically zoom, refocus, and self-correct while colle…
IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysis
Guo Tang, Songhan Jiang, Jinpeng Lu +2
Pathological images play an essential role in cancer prognosis, while survival analysis, which integrates computational techniques, can predict critical clinical events such as pat…
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
Jinpeng Lu, Linghan Cai, Yinda Chen +4
Lightweight 3D medical image segmentation remains constrained by a fundamental \textit{``efficiency / robustness conflict''}, particularly when processing complex anatomical struct…
Prototype-Guided Cross-Modal Knowledge Enhancement for Adaptive Survival Prediction
Fengchun Liu, Linghan Cai, Zhikang Wang +4
Histo-genomic multimodal survival prediction has garnered growing attention for its remarkable model performance and potential contributions to precision medicine. However, a signi…
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images
Songhan Jiang, Zhengyu Gan, Linghan Cai +2
Survival prediction, utilizing pathological images and genomic profiles, is increasingly important in cancer analysis and prognosis. Despite significant progress, precise survival…
Dynamic Pseudo Label Optimization in Point-Supervised Nuclei Segmentation
Ziyue Wang, Ye Zhang, Yifeng Wang +2
Deep learning has achieved impressive results in nuclei segmentation, but the massive requirement for pixel-wise labels remains a significant challenge. To alleviate the annotation…
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
Ye Zhang, Yifeng Wang, Zijie Fang +4
Weakly supervised segmentation methods have gained significant attention due to their ability to reduce the reliance on costly pixel-level annotations during model training. Howeve…
Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint
Linghan Cai, Shenjin Huang, Ye Zhang +2
Multiple instance learning (MIL) is a robust paradigm for whole-slide pathological image (WSI) analysis, processing gigapixel-resolution images with slide-level labels. As pioneeri…
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
Jinpeng Lu, Jingyun Chen, Linghan Cai +2
Positron emission tomography (PET) combined with computed tomography (CT) imaging is routinely used in cancer diagnosis and prognosis by providing complementary information. Automa…
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation
Ye Zhang, Linghan Cai, Ziyue Wang +1
Nuclei instance segmentation in histopathological images is of great importance for biological analysis and cancer diagnosis but remains challenging for two reasons. (1) Similar vi…
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
Ye Zhang, Ziyue Wang, Yifeng Wang +5
Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these method…
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images
Linghan Cai, Jianhao Huang, Zihang Zhu +2
Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cance…