6 papers
HiST: A Hierarchical Sparse Transformer for Cross-Modal Spatial Transcriptomics Modeling
Weiyi Wu, Xinwen Xu, Xingjian Diao +4
Spatial transcriptomics (ST) links gene expression with tissue morphology but remains expensive and low-throughput, motivating surrogates that infer expression from routine histolo…
Learning Spatial-Preserving Hierarchical Representations for Digital Pathology
Weiyi Wu, Xingjian Diao, Chunhui Zhang +4
Whole slide images (WSIs) pose fundamental computational challenges due to their gigapixel resolution and the sparse distribution of informative regions. Existing approaches often…
Learning Positive-Incentive Point Sampling in Neural Implicit Fields for Object Pose Estimation
Yifei Shi, Boyan Wan, Xin Xu +1
Learning neural implicit fields of 3D shapes is a rapidly emerging field that enables shape representation at arbitrary resolutions. Due to the flexibility, neural implicit fields…
Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis
Weiyi Wu, Xinwen Xu, Chongyang Gao +3
Whole slide images, with their gigapixel-scale panoramas of tissue samples, are pivotal for precise disease diagnosis. However, their analysis is hindered by immense data size and…
ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering
Xingjian Diao, Weiyi Wu, Keyi Kong +5
Visual Question Answering (VQA) is increasingly used in diverse applications ranging from general visual reasoning to safety-critical domains such as medical imaging and autonomous…
Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models
Weiyi Wu, Xinwen Xu, Chongyang Gao +4
Large Language Models (LLMs) have great potential in the field of health care, yet they face great challenges in adapting to rapidly evolving medical knowledge. This can lead to ou…