88 citations · 570 across the 27 of their papers we have counts for
8 papers · 1 filter
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-…
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…
Residual Channel Attention Network for Brain Glioma Segmentation
Yiming Yao, Peisheng Qian, Ziyuan Zhao +1
A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of interclass…
Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling
Yaxin Zhu, Peisheng Qian, Ziyuan Zhao +1
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinic…
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…
Cribriform pattern detection in prostate histopathological images using deep learning models
Malay Singh, Emarene Mationg Kalaw, Wang Jie +7
Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying st…