Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Zhu Heqin"'
Autor:
Tang, Fenghe, Xu, Ronghao, Yao, Qingsong, Fu, Xueming, Quan, Quan, Zhu, Heqin, Liu, Zaiyi, Zhou, S. Kevin
The generative self-supervised learning strategy exhibits remarkable learning representational capabilities. However, there is limited attention to end-to-end pre-training methods based on a hybrid architecture of CNN and Transformer, which can learn
Externí odkaz:
http://arxiv.org/abs/2408.05815
The Segment Anything Model (SAM) has achieved a notable success in two-dimensional image segmentation in natural images. However, the substantial gap between medical and natural images hinders its direct application to medical image segmentation task
Externí odkaz:
http://arxiv.org/abs/2311.10121
One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily i
Externí odkaz:
http://arxiv.org/abs/2306.07615
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples. However, searching optimal augmentation parameters for few-shot segmentatio
Externí odkaz:
http://arxiv.org/abs/2306.05107
Autor:
Huang, Zhen, Li, Han, Shao, Shitong, Zhu, Heqin, Hu, Huijie, Cheng, Zhiwei, Wang, Jianji, Zhou, S. Kevin
The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnosis and treatment of pelvic diseases. Although PXRs have the advantages
Externí odkaz:
http://arxiv.org/abs/2305.04294
Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based on CNN and specialized for a single domain say associated with a partic
Externí odkaz:
http://arxiv.org/abs/2203.06433
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than as input, depth-supervised convolutional neural networks (CNN) have ac
Externí odkaz:
http://arxiv.org/abs/2203.06429
Contrastive learning based methods such as cascade comparing to detect (CC2D) have shown great potential for one-shot medical landmark detection. However, the important cue of relative distance between landmarks is ignored in CC2D. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2203.01687
Publikováno v:
In Medical Image Analysis August 2024 96
Detecting anatomical landmarks in medical images plays an essential role in understanding the anatomy and planning automated processing. In recent years, a variety of deep neural network methods have been developed to detect landmarks automatically.
Externí odkaz:
http://arxiv.org/abs/2103.04657