most citedMMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation

24 citations · 47 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SP20231 cited

Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning

Rui Liu, Yuanyuan Chen, Anran Li +3

Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes co…

q-bio.MN20232 cited

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…

cs.CV20239 cited

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…

cs.MM2023

Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5

Su Zhang, Ziyuan Zhao, Cuntai Guan

We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3,…

eess.IV20231 cited

MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo +2

Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentati…

eess.IV202210 cited

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…