Publications (10)
HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction
Chen Zhang, Yilu An, Ying Chen +7
Spatial Transcriptomics (ST) merges the benefits of pathology images and gene expression, linking molecular profiles with tissue structure to analyze spot-level function comprehens…
SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction
Ying Chen, Jiajing Xie, Yuxiang Lin +3
Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not ful…
ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
Yuxiang Lin, Ling Luo, Ying Chen +5
Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. This setting m…
Federated Learning with Fair Averaging
Zheng Wang, Xiaoliang Fan, Jianzhong Qi +3
Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the…
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
Hao Li, Ying Chen, Yifei Chen +5
Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable perfo…
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
Ying Chen, Guoan Wang, Yuanfeng Ji +7
Despite the progress made by multimodal large language models (MLLMs) in computational pathology, they remain limited by a predominant focus on patch-level analysis, missing essent…