1 citations · 1 across the 10 of their papers we have counts for
10 papers
Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images
Liangrui Pan, Jiadi Luo, Yuxuan Xiao +13
Accurate intraoperative and postoperative diagnosis of spread through air spaces (STAS) is essential for guiding surgical decisions and postoperative management in lung cancer. How…
STAMP: Multi-pattern Attention-aware Multiple Instance Learning for STAS Diagnosis in Multi-center Histopathology Images
Liangrui Pan, xiaoyu Li, Guang Zhu +6
Spread through air spaces (STAS) constitutes a novel invasive pattern in lung adenocarcinoma (LUAD), associated with tumor recurrence and diminished survival rates. However, large-…
PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset
Liangrui Pan, Qingchun Liang, Shen Zhao +2
Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognostic assessment. Faced with regiona…
DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset
Liangrui Pan, Xingchen Li, Zhongyi Chen +2
Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, rapidly and accurately obtaining t…
SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis
Liangrui Pan, Xiaoyu Li, Yutao Dou +4
Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathol…
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
Liangrui Pan, Qingchun Liang, Wenwu Zeng +6
Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard…
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation
Liangrui Pan, Mao Huang, Lian Wang +2
Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning,…
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival
Liangrui Pan, Yijun Peng, Yan Li +4
Integrating the different data modalities of cancer patients can significantly improve the predictive performance of patient survival. However, most existing methods ignore the sim…
Opportunities and challenges in the application of large artificial intelligence models in radiology
Liangrui Pan, Zhenyu Zhao, Ying Lu +4
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI…
SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
Liangrui Pan, Yijun Peng, Yan Li +5
Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and signifi…