32 papers
Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model
Yijun Yang, Ruiqiang Xiao, Lijie Hu +4
Deep learning has revolutionized medical image analysis, delivering exceptional diagnostic accuracy across diverse applications. Yet, the lack of interpretability in its decision-m…
Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs
Xuxiang Zhao, Angelica I. Aviles-Rivero
Spectral neural operators achieve strong performance for PDE learning, but rely on fixed global bases that limit their ability to represent spatially heterogeneous and multiscale d…
CATO: Charted Attention for Neural PDE Operators
Chun-Wun Cheng, Sifan Wang, Carola-Bibiane Schönlieb +1
Neural operators have emerged as powerful data-driven solvers for PDEs, offering substantial acceleration over classical numerical methods. However, existing transformer-based oper…
Do Neural Operators Forget Geometry? The Forgetting Hypothesis in Deep Operator Learning
Yanming Xia, Angelica I. Aviles-Rivero
Neural operators perform well on structured domains, yet their behaviour on irregular geometries remains poorly understood. We show that this limitation is not merely an encoding i…
Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series
Guoqi Yu, Juncheng Wang, Chen Yang +3
Accurate analysis of medical time series (MedTS) data, such as electroencephalography (EEG) and electrocardiography (ECG), plays a pivotal role in healthcare applications, includin…
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…