87 citations · 299 across the 14 of their papers we have counts for
15 papers
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…
Leveraging LLM Agents for Automated Video Game Testing
Chengjia Wang, Lanling Tang, Ming Yuan +3
Testing MMORPGs (Massively Multiplayer Online Role-Playing Games) is a critical yet labor-intensive task in game development due to their complexity and frequent updating nature. T…
REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers
Yuepeng Jiang, Ziqian Ning, Shuai Wang +5
In real-world voice conversion applications, environmental noise in source speech and user demands for expressive output pose critical challenges. Traditional ASR-based methods ens…
A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization
Peng Qi, Wenxi Qu, Tianliang Yao +5
Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure that improves coronary blood flow and treats coronary artery disease. Although PCI typically requires 2D…
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models
Yuchen Mao, Hongwei Li, Yinyi Lai +4
Large-scale vision models like SAM have extensive visual knowledge, yet their general nature and computational demands limit their use in specialized tasks like medical image segme…
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…
Explainable unsupervised multi-modal image registration using deep networks
Chengjia Wang, Giorgos Papanastasiou
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims…
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…
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Chengjia Wang, Guang Yang, Giorgos Papanastasiou
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional cli…
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias +1
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the…
Learning to synthesise the ageing brain without longitudinal data
Tian Xia, Agisilaos Chartsias, Chengjia Wang +1
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data)…
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…