554 citations · 605 across the 20 of their papers we have counts for
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Triple-View Feature Learning for Medical Image Segmentation
Ziyang Wang, Irina Voiculescu
Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit promising performance in medical image segmentation, but come with a high labelling cost. We propose T…
When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation
Ziyang Wang, Tianze Li, Jian-Qing Zheng +1
Due to the lack of quality annotation in medical imaging community, semi-supervised learning methods are highly valued in image semantic segmentation tasks. In this paper, an advan…
Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images
Wenqiang Li, YM Tang, Ziyang Wang +2
Automatic medical image segmentation based on Computed Tomography (CT) has been widely applied for computer-aided surgery as a prerequisite. With the development of deep learning t…
Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT
Ziyang Wang, Irina Voiculescu
COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world. Chest CT is essential in prognostication, diagnosing this disea…
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels
Ziyang Wang, Zhengdong Zhang, Irina Voiculescu
Segmentation algorithms for medical images are widely studied for various clinical and research purposes. In this paper, we propose a new and efficient method for medical image seg…
Deep Learning in Medical Ultrasound Image Segmentation: a Review
Ziyang Wang
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. I…