2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images
Liangrui Pan, Lian Wang, Zhichao Feng +2
Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality ma…
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
Liangrui Pan, Yutao Dou, Zhichao Feng +2
Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In thi…
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
Liangrui Pan, Xiang Wang, Qingchun Liang +4
Background and Objective: Given the high heterogeneity and clinical diversity of cancer, substantial variations exist in multi-omics data and clinical features across different can…
MGTUNet: An new UNet for colon nuclei instance segmentation and quantification
Liangrui Pan, Lian Wang, Zhichao Feng +3
Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer…