92 citations · 328 across the 84 of their papers we have counts for
91 papers
Truecell reproduces R Seurat's single-cell analysis outputs natively in the Python ecosystem
Nuzla Ismail, Shanika Amarasoma, Michelle Galeas-Pena +1
Single-cell RNA-sequencing analyses predominantly use either Seurat (R) or Scanpy (Python), with the choice often driven by programming language preference. However, by default, th…
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…
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…
ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection
Chun-Wun Cheng, Yanqi Cheng, Peiyuan Jing +4
Medical image segmentation commonly relies on U-shaped encoder-decoder architectures such as U-Net, where skip connections preserve fine spatial detail by injecting high-resolution…
3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising
Peiyuan Jing, Yue Yang, Chun-Wun Cheng +8
Low-dose Positron Emission Tomography (PET) imaging reduces patient radiation exposure but suffers from increased noise that degrades image quality and diagnostic reliability. Alth…
SpectraKAN: Conditioning Spectral Operators
Chun-Wun Cheng, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
Spectral neural operators, particularly Fourier Neural Operators (FNO), are a powerful framework for learning solution operators of partial differential equations (PDEs) due to the…
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…
Product Interaction: An Algebraic Formalism for Deep Learning Architectures
Haonan Dong, Chun-Wun Cheng, Angelica I. Aviles-Rivero
Modern neural architectures are largely developed as distinct empirical constructions, despite recurring principles across convolutional, attention, state-space, and equivariant mo…
Training-Free Test-Time Adaptation with Brownian Distance Covariance in Vision-Language Models
Yi Zhang, Chun-Wun Cheng, Angelica I. Aviles-Rivero +2
Vision-language models suffer performance degradation under domain shift, limiting real-world applicability. Existing test-time adaptation methods are computationally intensive, re…
AlphaFace: High Fidelity and Real-time Face Swapper Robust to Facial Pose
Jongmin Yu, Hyeontaek Oh, Zhongtian Sun +3
Existing face-swapping methods often deliver competitive results in constrained settings but exhibit substantial quality degradation when handling extreme facial poses. To improve…
LEGATO: Good Identity Unlearning Is Continuous
Qiang Chen, Chun-Wun Cheng, Xiu Su +5
Machine unlearning has become a crucial role in enabling generative models trained on large datasets to remove sensitive, private, or copyright-protected data. However, existing ma…
DNA-Prior: Unsupervised Denoise Anything via Dual-Domain Prior
Yanqi Cheng, Chun-Wun Cheng, Jim Denholm +5
Medical imaging pipelines critically rely on robust denoising to stabilise downstream tasks such as segmentation and reconstruction. However, many existing denoisers depend on larg…
PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils
Chun-Wun Cheng, Bin Dong, Carola-Bibiane Schönlieb +1
Neural operator models for solving partial differential equations (PDEs) often rely on global mixing mechanisms-such as spectral convolutions or attention-which tend to oversmooth…
NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting
Huibo Xu, Likang Wu, Xianquan Wang +4
Time series forecasting is a fundamental task with broad applications, yet conventional methods often treat data as discrete sequences, overlooking their origin as noisy samples of…
Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets
Mikhail Kennerley, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb +1
Combining multiple object detection datasets offers a path to improved generalisation but is hindered by inconsistencies in class semantics and bounding box annotations. Some metho…
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction
Zhenxuan Zhang, Lipei Zhang, Yanqi Cheng +10
In motion-robust magnetic resonance imaging (MRI), slice-to-volume reconstruction is critical for recovering anatomically consistent 3D brain volumes from 2D slices, especially und…
Deep Spectral Prior
Yanqi Cheng, Xuxiang Zhao, Tieyong Zeng +3
We introduce the Deep Spectral Prior (DSP), a new framework for unsupervised image reconstruction that operates entirely in the complex frequency domain. Unlike the Deep Image Prio…
Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis
Zhongying Deng, Haoyu Wang, Ziyan Huang +6
Brain diseases, such as Alzheimer's disease and brain tumors, present profound challenges due to their complexity and societal impact. Recent advancements in brain foundation model…
RSFR: A Coarse-to-Fine Reconstruction Framework for Diffusion Tensor Cardiac MRI with Semantic-Aware Refinement
Jiahao Huang, Fanwen Wang, Pedro F. Ferreira +14
Cardiac diffusion tensor imaging (DTI) offers unique insights into cardiomyocyte arrangements, bridging the gap between microscopic and macroscopic cardiac function. However, its c…
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting
Guoqi Yu, Yaoming Li, Juncheng Wang +4
Recent advancements have progressively incorporated frequency-based techniques into deep learning models, leading to notable improvements in accuracy and efficiency for time series…
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction
Lipei Zhang, Rui Sun, Zhongying Deng +3
Variations in Magnetic resonance imaging (MRI) scanners and acquisition protocols cause distribution shifts that degrade reconstruction performance on unseen data. Test-time adapta…