342 citations · 480 across the 33 of their papers we have counts for
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Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views
Dong Wei, Kai Ma, Yefeng Zheng
View planning for the acquisition of cardiac magnetic resonance imaging (CMR) requires acquaintance with the cardiac anatomy and remains a challenging task in clinical practice. Ex…
Residual Moment Loss for Medical Image Segmentation
Quanziang Wang, Renzhen Wang, Yuexiang Li +3
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segme…
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Xinpeng Xie, Jiawei Chen, Yuexiang Li +3
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative…
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
Wenting Chen, Shuang Yu, Junde Wu +5
Retinal artery/vein (A/V) classification lays the foundation for the quantitative analysis of retinal vessels, which is associated with potential risks of various cardiovascular an…
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
Yuexiang Li, Jiawei Chen, Xinpeng Xie +2
Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks fo…
Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification
Wenao Ma, Shuang Yu, Kai Ma +3
Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conve…
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
Xing Tao, Yuexiang Li, Wenhui Zhou +2
Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days fo…
X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
Xingde Ying, Heng Guo, Kai Ma +3
Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is…