no code implementations • 14 Mar 2024 • Lipei Zhang, Yanqi Cheng, Lihao Liu, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
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.
no code implementations • 22 Nov 2023 • Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
no code implementations • 21 Nov 2023 • Zhenda Shen, Yanqi Cheng, Raymond H. Chan, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Implicit neural representations (INRs) have garnered significant interest recently for their ability to model complex, high-dimensional data without explicit parameterisation.
no code implementations • 16 Nov 2023 • Lihao Liu, Yanqi Cheng, Dongdong Chen, Jing He, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
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.
no code implementations • 31 Oct 2023 • Sergio Calvo-Ordonez, Chun-Wun Cheng, Jiahao Huang, Lipei Zhang, Guang Yang, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
no code implementations • 18 Mar 2023 • Shujun Wang, Angelica I Aviles-Rivero, Zoe Kourtzi, Carola-Bibiane Schönlieb
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.
no code implementations • 14 Mar 2023 • Jing Zou, Noémie Debroux, Lihao Liu, Jing Qin, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
We propose a novel framework for deformable image registration.
no code implementations • 1 Feb 2023 • Chun-Wun Cheng, Christina Runkel, Lihao Liu, Raymond H Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations.
1 code implementation • 17 Nov 2022 • Zhongying Deng, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
no code implementations • 17 Nov 2022 • Zhongying Deng, Yanqi Chen, Lihao Liu, Shujun Wang, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
no code implementations • CVPR 2023 • Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
no code implementations • 20 Jan 2021 • Tao Wei, Angelica I Aviles-Rivero, Shuo Wang, Yuan Huang, Fiona J Gilbert, Carola-Bibiane Schönlieb, Chang Wen Chen
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
1 code implementation • 17 Nov 2020 • Lihao Liu, Angelica I Aviles-Rivero, Carola-Bibiane Schönlieb
Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.