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Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Qian Yu, Lei Qi, Luping Zhou +5
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these…
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
Zekun Li, Wei Zhao, Feng Shi +9
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the c…
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation
Xiaoting Han, Lei Qi, Qian Yu +4
Unsupervised domain adaptation (UDA) methods have shown their promising performance in the cross-modality medical image segmentation tasks. These typical methods usually utilize a…
MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling
Kelei He, Chunfeng Lian, Ehsan Adeli +5
Fully convolutional networks (FCNs), including UNet and VNet, are widely-used network architectures for semantic segmentation in recent studies. However, conventional FCN is typica…
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Feng Shi, Jun Wang, Jun Shi +6
(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over…
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation
Qian Yu, Yinghuan Shi, Yefeng Zheng +3
Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patie…