421 citations · 1.1k across the 23 of their papers we have counts for
8 papers · 1 filter
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo +2
Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentati…
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu +3
While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-…
Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition
Mengjiao Hu, Xudong Jiang, Kang Sim +2
Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for…
OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation
Amrest Chinkamol, Vetit Kanjaras, Phattarapong Sawangjai +5
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the…
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
Ziyuan Zhao, Andong Zhu, Zeng Zeng +2
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especial…
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
Ziyuan Zhao, Jinxuan Hu, Zeng Zeng +4
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in cli…
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Ziyuan Zhao, Wenjing Lu, Zeng Zeng +3
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements. Recently, active learning (AL) strategi…
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
Ziyuan Zhao, Kaixin Xu, Shumeng Li +2
The success of deep convolutional neural networks (DCNNs) benefits from high volumes of annotated data. However, annotating medical images is laborious, expensive, and requires hum…