Search Results for author:
Found 31 papers, 9 papers with code
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting
However, the Mid-Frequency Spectrum Gap in the real-world time series, where the energy is concentrated at the low-frequency region while the middle-frequency band is negligible, hinders the ability of existing deep learning models to extract the crucial frequency information.
NODE-Adapter: Neural Ordinary Differential Equations for Better Vision-Language Reasoning
To fully leverage both visual and textual modalities and estimate class prototypes more effectively and accurately, we divide our method into two stages: cross-modal prototype construction and cross-modal prototype optimization using neural ordinary differential equations.
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation.
MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models
Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing world.
Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis
Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life.
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
Although ViWS-Net is proposed to remove adverse weather conditions in videos with a single set of pre-trained weights, it is seriously blinded by seen weather at train-time and degenerates when coming to unseen weather during test-time.
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks.
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations.
Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation
In this work, we propose the first framework for restoring videos from all adverse weather conditions by developing a video adverse-weather-component suppression network (ViWS-Net).
MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data.
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study
Our results indicate that the models we discussed in this study can be applied for clinical use at an acceleration factor (AF) of $\times 2$ and $\times 4$, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores.
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
This is the question that we address in this work.
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.
PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework.
Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based Vertex Embeddings
Firstly, we prove that, under some weak assumptions, vertex embeddings derived from random walks do indeed converge both in the single limit of the number of random walks $N \to \infty$ and in the double limit of both $N$ and the length of each random walk $L\to\infty$.
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect.
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously.
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification
In this work, we propose an all-in-one framework for deep semi-supervised classification focusing on graph based approaches, which up to our knowledge it is the first time that an approach with minimal labels has been shown to such an unprecedented scale with medical data.
Dim the Lights! -- Low-Rank Prior Temporal Data for Specular-Free Video Recovery
Our solution is a twostep approach that allows for both detection and restoration of the damaged regions on video data.
Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently.
Dynamic Spectral Residual Superpixels
We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge.
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction
Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion.
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.
Peekaboo - Where are the Objects? Structure Adjusting Superpixels
This paper addresses the search for a fast and meaningful image segmentation in the context of $k$-means clustering.
