87 citations · 299 across the 14 of their papers we have counts for
7 papers · 1 filter
Water Reflection Detection Using Symmetric Attention
Shuxuan Yao, Chengjia Wang, Jianyuan Sun +2
Reflections of water pose a significant challenge for computer vision systems, as standard deep learning models frequently confuse objects with their mirror images, producing spuri…
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis
Yuchen Mao, Hongwei Li, Wei Pang +3
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored th…
Is attention all you need in medical image analysis? A review
Giorgos Papanastasiou, Nikolaos Dikaios, Jiahao Huang +2
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias +3
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway. This paper presents an unsupervised deep inter-modali…
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou +2
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformati…
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
Chengjia Wang, Tom MacGillivray, Gillian Macnaught +2
Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances. Howe…