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Lizhi Shao

3 accepted papers

2025

Learning Heterogeneous Tissues with Mixture of Experts for Gigapixel Whole Slide Images

CVPR 2025poster

Analyzing gigapixel Whole Slide Images (WSIs) is challenging due to the complex pathological tissue environment and the absence of target-driven domain knowledge. Previous methods incorporated pathological priors to mitigate this issue but relied on additional inference steps and specialized workflo…

2024

Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images

NeurIPS 2024poster

Survival prediction is a significant challenge in cancer management. Tumor micro-environment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple…

Cited by 3SourcePDFScholar
2024

Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images

AAAI 2024technical

Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federat…