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20232026
most citedDEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data

2 citations · 3 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

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…

cs.CV2025

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-…

cs.CV2025

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…

cs.CV2024★ 1 cited

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…

cs.CV2024

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…

cs.CV2024

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…