309 citations · 584 across the 12 of their papers we have counts for
12 papers
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition
Jia-Xin Zhuang, Jiabin Cai, Jianguo Zhang +2
Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and…
Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach
Qingqiao Hu, Hongwei Li, Jianguo Zhang
Medical image synthesis has attracted increasing attention because it could generate missing image data, improving diagnosis and benefits many downstream tasks. However, so far the…
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Hongwei Li, Fei-Fei Xue, Krishna Chaitanya +5
Radiomic representations can quantify properties of regions of interest in medical image data. Classically, they account for pre-defined statistics of shape, texture, and other low…
Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation
Jingkun Chen, Wenqi Li, Hongwei Li +1
Multi-modal medical image segmentation plays an essential role in clinical diagnosis. It remains challenging as the input modalities are often not well-aligned spatially. Existing…
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation
Hongwei Li, Jianguo Zhang, Bjoern Menze
Tackling domain shifts in multi-centre and multi-vendor data sets remains challenging for cardiac image segmentation. In this paper, we propose a generalisable segmentation framewo…
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns
Hongwei Li, Timo Loehr, Anjany Sekuboyina +3
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying…
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation
Jingkun Chen, Hongwei Li, Jianguo Zhang +1
Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scan…
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
Hongwei Li, Johannes C. Paetzold, Anjany Sekuboyina +5
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e.g. due to motion). Naturally, the idea ar…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation
Hongwei Li, Jianguo Zhang, Mark Muehlau +2
Segmentation of both large and small white matter hyperintensities/lesions in brain MR images is a challenging task which has drawn much attention in recent years. We propose a mul…
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
Hongwei Li, Gongfa Jiang, Jianguo Zhang +4
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In t…
Deep CNNs for HEp-2 Cells Classification : A Cross-specimen Analysis
Hongwei Li, Wei-Shi Zheng, Jianguo Zhang
Automatic classification of Human Epithelial Type-2 (HEp-2) cells staining patterns is an important and yet a challenging problem. Although both shallow and deep methods have been…