Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Zhu, Qikui"'
Coordinated Transformer with Position \& Sample-aware Central Loss for Anatomical Landmark Detection
Heatmap-based anatomical landmark detection is still facing two unresolved challenges: 1) inability to accurately evaluate the distribution of heatmap; 2) inability to effectively exploit global spatial structure information. To address the computati
Externí odkaz:
http://arxiv.org/abs/2305.11338
The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol is too expensive to implement in many real-world projects. In this st
Externí odkaz:
http://arxiv.org/abs/2206.15328
Publikováno v:
In Engineering Applications of Artificial Intelligence June 2024 132
Autor:
Sun, Feng, Kumar V, Ajith, Yang, Guanci, Zhu, Qikui, Zhang, Yiyun, Zhang, Ansi, Makwana, Dhruv
Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training. Besides, the adjacency matrix of GCNs is stable, which makes the data processing strategy cannot efficiently adjust t
Externí odkaz:
http://arxiv.org/abs/2108.07481
Autor:
Zhu, Qikui, Li, Liang, Hao, Jiangnan, Zha, Yunfei, Zhang, Yan, Cheng, Yanxiang, Liao, Fei, Li, Pingxiang
Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between images
Externí odkaz:
http://arxiv.org/abs/2010.04920
Graph Convolutional Networks (GCNs) have been successfully applied to analyze non-grid data, where the classical convolutional neural networks (CNNs) cannot be directly used. One similarity shared by GCNs and CNNs is the requirement of massive amount
Externí odkaz:
http://arxiv.org/abs/2006.02380
Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited th
Externí odkaz:
http://arxiv.org/abs/1912.02417
Graph Convolutional Networks (GCNs) have made significant advances in semi-supervised learning, especially for classification tasks. However, existing GCN based methods have two main drawbacks. First, to increase the receptive field and improve the r
Externí odkaz:
http://arxiv.org/abs/1911.04978
Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images still faces several challenges. For instance
Externí odkaz:
http://arxiv.org/abs/1902.08128
Akademický článek
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