Search Results for author:
Found 13 papers, 2 papers with code
Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation
Recent advancements in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information.
Single-Shot Plug-and-Play Methods for Inverse Problems
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
TRIDENT: The Nonlinear Trilogy for Implicit Neural Representations
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation.
Traffic Video Object Detection using Motion Prior
In this work, we propose two innovative methods to exploit the motion prior and boost the performance of both fully-supervised and semi-supervised traffic video object detection.
The Missing U for Efficient Diffusion Models
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
HGIB: Prognosis for Alzheimer's Disease via Hypergraph Information Bottleneck
We demonstrate, through extensive experiments on ADNI, that our proposed HGIB framework outperforms existing state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis.
Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
We propose a novel framework for deformable image registration.
Continuous U-Net: Faster, Greater and Noiseless
Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations.
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Cancer-no cancer per image classification
Image Classification
+3
Contrastive Registration for Unsupervised Medical Image Segmentation
Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.
