5 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
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…
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…
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…