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pro vyhledávání: '"Zhao, Ziyuan"'
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 methods have demonstrated promising outcomes owing to the inherent gr
Externí odkaz:
http://arxiv.org/abs/2404.10450
Autor:
Zhao, Ziyuan, Qian, Peisheng, Yang, Xulei, Zeng, Zeng, Guan, Cuntai, Tam, Wai Leong, Li, Xiaoli
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods suffer from significant performance degradation un
Externí odkaz:
http://arxiv.org/abs/2305.08316
Publikováno v:
Medical Image Computing and Computer Assisted Intervention, MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham
Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achieved promising cross-modality medical image segmentation by transferri
Externí odkaz:
http://arxiv.org/abs/2305.06978
Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain
Externí odkaz:
http://arxiv.org/abs/2303.15826
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, which employs the leader-follower attention. The second model has the
Externí odkaz:
http://arxiv.org/abs/2303.10335
A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference. However, not much prior efforts have been made to quantize and accelerate the b
Externí odkaz:
http://arxiv.org/abs/2303.02347
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-labeled datasets, which are difficult to curate due to the expert-driv
Externí odkaz:
http://arxiv.org/abs/2212.02078
Publikováno v:
33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022. BMVA Press, 2022. URL https://bmvc2022.mpi-inf.mpg.de/0916.pdf
Deep learning has achieved notable success in 3D object detection with the advent of large-scale point cloud datasets. However, severe performance degradation in the past trained classes, i.e., catastrophic forgetting, still remains a critical issue
Externí odkaz:
http://arxiv.org/abs/2212.02057
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP)
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, especially in clinical practice. On the other hand, high-accuracy deep models
Externí odkaz:
http://arxiv.org/abs/2207.01900
Autor:
Zhao, Ziyuan, Hu, Jinxuan, Zeng, Zeng, Yang, Xulei, Qian, Peisheng, Veeravalli, Bharadwaj, Guan, Cuntai
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP)
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in clinical practice due to extensive expertise requirements and costly labe
Externí odkaz:
http://arxiv.org/abs/2207.01883