87 citations · 106 across the 14 of their papers we have counts for
5 papers · 1 filter
Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs
Chun-Wun Cheng, Jiahao Huang, Yi Zhang +3
Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have e…
CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
Liutao Yang, Jiahao Huang, Guang Yang +1
Sparse views X-ray computed tomography has emerged as a contemporary technique to mitigate radiation dose. Because of the reduced number of projection views, traditional reconstruc…
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
Liutao Yang, Jiahao Huang, Yingying Fang +4
Sparse-View Computed Tomography (SVCT) offers low-dose and fast imaging but suffers from severe artifacts. Optimizing the sampling strategy is an essential approach to improving th…
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
Jiahao Huang, Liutao Yang, Fanwen Wang +5
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks. This study introduces MambaMIR, a Mamba-based model…
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
Andrey Bryutkin, Jiahao Huang, Zhongying Deng +3
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework use…