19 papers
HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction
Ruicheng Yuan, Zhenxuan Zhang, Anbang Wang +5
Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet…
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
Yinzhe Wu, Fanwen Wang, Zhenxuan Zhang +3
Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-space data while preserving st…
Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores
Congren Dai, Yue Yang, Krinos Li +12
Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision--Lang…
Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning
Kinhei Lee, Peiyuan Jing, Zhenxuan Zhang +5
Large scale vision language models have shown promise in automating chest Xray interpretation, yet their clinical utility remains limited by a gap between model outputs and radiolo…
Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database
Zi Wang, Mingkai Huang, Zhang Shi +61
Multimodal cardiovascular magnetic resonance (CMR) imaging provides comprehensive and non-invasive insights into cardiovascular disease (CVD) diagnosis and underlying mechanisms. D…
From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation
Tao Wang, Zhenxuan Zhang, Yuanbo Zhou +5
The effectiveness of convolutional neural networks in medical image segmentation relies on large-scale, high-quality annotations, which are costly and time-consuming to obtain. Eve…