activity
20202024
most citedDSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

88 citations · 242 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20243 cited

Graph Neural Networks for Protein-Protein Interactions -- A Short Survey

Mingda Xu, Peisheng Qian, Ziyuan Zhao +4

Protein-protein interactions (PPIs) play key roles in a broad range of biological processes. Numerous strategies have been proposed for predicting PPIs, and among them, graph-based…

eess.IV202283 cited

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-…

eess.IV202238 cited

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…

cs.CV202188 cited

DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

Ziyuan Zhao, Zeng Zeng, Kaixin Xu +2

Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Thi…

cs.CV202030 cited

Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading

Ziyuan Zhao, Kartik Chopra, Zeng Zeng +1

Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading bas…