88 citations · 533 across the 20 of their papers we have counts for
20 papers
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…
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction
Ziyuan Zhao, Peisheng Qian, Xulei Yang +4
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing de…
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
Ziyuan Zhao, Fangcheng Zhou, Zeng Zeng +2
Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achi…
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-…
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…
Adaptive Mean-Residue Loss for Robust Facial Age Estimation
Ziyuan Zhao, Peisheng Qian, Yubo Hou +1
Automated facial age estimation has diverse real-world applications in multimedia analysis, e.g., video surveillance, and human-computer interaction. However, due to the randomness…
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…
Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading
Peisheng Qian, Ziyuan Zhao, Cong Chen +2
Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients. However, vision loss occurs primarily in the late stages of DR, and the symptoms of visu…
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
Ziyuan Zhao, Zeyu Ma, Yanjie Liu +2
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tr…
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation
Shumeng Li, Ziyuan Zhao, Kaixin Xu +2
Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain…
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…
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…
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Kaixin Xu, Ziyuan Zhao, Jiapan Gu +5
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle…
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
Jiapan Gu, Ziyuan Zhao, Zeng Zeng +10
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene…
Multi-Target Deep Learning for Algal Detection and Classification
Peisheng Qian, Ziyuan Zhao, Haobing Liu +6
Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive t…
Deeply Supervised Active Learning for Finger Bones Segmentation
Ziyuan Zhao, Xiaoyan Yang, Bharadwaj Veeravalli +1
Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segm…
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Ziyuan Zhao, Kerui Zhang, Xuejie Hao +4
Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is…
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Ziyuan Zhao, Xiaoman Zhang, Cen Chen +6
Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep…