21 citations · 45 across the 12 of their papers we have counts for
13 papers
International Multi-Centre Validation of Unsupervised Domain Adaptation for Precise Discrimination between Normal and Abnormal Retinal Development
Zhanhan Tu, Nikhil Reddy, Helen J. Kuht +27
Arrested retinal development can cause childhood visual impairment, but grading optical coherence tomography scans is difficult to standardise across devices and centres. We develo…
ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment
Xianye Xiao, Yulong Zou, Yujie Luo +6
In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for evaluating CT image quality h…
MGML: A Plug-and-Play Meta-Guided Multi-Modal Learning Framework for Incomplete Multimodal Brain Tumor Segmentation
Yulong Zou, Bo Liu, Cun-Jing Zheng +6
Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multi…
ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training
Siyue Li, Yongcheng Yao, Junru Zhong +9
Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contr…
WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in Spatial-Frequency Domain
Jilan Cheng, Guoli Long, Zeyu Zhang +6
Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment. Optical Coherence Tomography (OCT) has become…
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation
Bo Liu, Yudong Zhang, Shuihua Wang +2
Retinal vascular morphology is crucial for diagnosing diseases such as diabetes, glaucoma, and hypertension, making accurate segmentation of retinal vessels essential for early int…
Review of AlexNet for Medical Image Classification
Wenhao Tang, Junding Sun, Shuihua Wang +1
In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models…
EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection
Xiang Li, Long Lan, Husam Lahza +5
In the face of rapid advances in medical imaging, cross-domain adaptive medical image detection is challenging due to the differences in lesion representations across various medic…
A Novel Human-Based Meta-Heuristic Algorithm: Dragon Boat Optimization
Xiang Li, Long Lan, Husam Lahza +5
(Aim) Dragon Boat Racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-…
TBDLNet: a network for classifying multidrug-resistant and drug-sensitive tuberculosis
Ziquan Zhu, Jing Tao, Shuihua Wang +2
This paper proposes applying a novel deep-learning model, TBDLNet, to recognize CT images to classify multidrug-resistant and drug-sensitive tuberculosis automatically. The pre-tra…
MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis
Lin Yang, Shuihua Wang, Yudong Zhang
Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 di…
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images
Shuihua Wang, Ahmed M. S. E. K Abdelaty, Kelly Parke +3
A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular…
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes
Haiyan Wang, Xuejian Rong, Liang Yang +3
The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects.…