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科研机构

自动化研究所 [5]

湖南大学 [5]

北京航空航天大学 [4]

山东大学 [4]

沈阳自动化研究所 [4]

武汉大学 [3]

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期刊论文 [30]

会议论文 [5]

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2019 [35]

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浏览/检索结果: 共35条，第1-10条

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发表日期：2019

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	Learning from adversarial medical images for X-ray breast mass segmentation 期刊论文 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2019, 卷号: 180, 页码: 13 作者: Shen, Tianyu; Gou, Chao; Wang, Fei-Yue; He, Zilong; Chen, Weiguo 收藏 \| 浏览/下载：60/0 \| 提交时间：2019/12/16 Medical image synthesis Generative adversarial network X-ray breast mass Lesion segmentation
	Bag of Tricks for 3D MRI Brain Tumor Segmentation 会议论文深圳, 2019-10 作者: Zhao YX(赵元兴); Zhang YM(张燕明); Liu CL(刘成林) 收藏 \| 浏览/下载：6/0 \| 提交时间：2022/08/18
	Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning 期刊论文 IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 11, 页码: 1922-1930 作者: Yang, Menglin; Li, Ding; Zhang, Wensheng 收藏 \| 浏览/下载：61/0 \| 提交时间：2019/12/16 feature extraction diseases medical image processing patient diagnosis image segmentation image classification supervised learning image annotation thoracic disease diagnosis weakly supervised learning computer-aided diagnosis coarse image-level annotations pathological area nonlocal aware features fine-grained expertise clinical diagnosis visualisation method chest X-ray dataset spatial nonlocal attention mechanism ResNet-SNA
	Local difference-based active contour model for medical image segmentation and bias correction 期刊论文 IET Image Processing, 2019, 卷号: 13, 期号: 10, 页码: 1755-1762 作者: Niu, Yuefeng; Cao, Jianzhong 收藏 \| 浏览/下载：59/0 \| 提交时间：2019/09/25 image segmentation medical image processing two-phase model medical images LBDE model precise segmentation results comparative models local difference-based active contour model medical image segmentation bias correction local bias field difference estimation model smooth orthogonal basis functions clustering criterion function measured image local region accurate segmentation results level set evolution process
	Local difference-based active contour model for medical image segmentation and bias correction 期刊论文 IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 10, 页码: 1755-1762 作者: Niu, Yuefeng; Cao, Jianzhong 收藏 \| 浏览/下载：21/0 \| 提交时间：2020/06/05 image segmentation medical image processing two-phase model medical images LBDE model precise segmentation results comparative models local difference-based active contour model medical image segmentation bias correction local bias field difference estimation model smooth orthogonal basis functions clustering criterion function measured image local region accurate segmentation results level set evolution process
	Automatic Carotid Artery Detection Using Attention Layer Region-Based Convolution Neural Network 期刊论文 INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS, 2019, 卷号: 16, 期号: 4, 页码: 17 作者: Wang, Xiaoyan; Zhong, Xingyu; Xia, Ming; Jiang, Weiwei; Huang, Xiaojie 收藏 \| 浏览/下载：73/0 \| 提交时间：2019/12/16 Object detection convolutional neural networks ensemble feature maps
	BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation 期刊论文 INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2019, 卷号: 126, 页码: 105-113 作者: Zhang, Yujun; Li, Tao; Gao, Yingqi; Kang, Hong; Wang, Kai 收藏 \| 浏览/下载：87/0 \| 提交时间：2019/08/16 Vessel segmentation Fundus image Deep supervision Short connection
	A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images 期刊论文 SCIENTIFIC REPORTS, 2019, 卷号: 9, 页码: 10 作者: Jiang, Taijiao; Wang, Kuansong; Guo, Wei; Guo, Zichao; Wang, Xiangdong 收藏 \| 浏览/下载：60/0 \| 提交时间：2019/04/03
	Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network 期刊论文 IEEE ACCESS, 2019, 卷号: 7, 页码: 16257-16263 作者: Zeng, Nianyin; Li, Han; Li, Yurong; Luo, Xin 收藏 \| 浏览/下载：47/0 \| 提交时间：2019/03/25 Gold immunochromatographic strip quantitative analysis image segmentation CNN
	Paraspinal Muscle Segmentation Based on Deep Neural Network 期刊论文 SENSORS, 2019, 卷号: 19, 期号: 12, 页码: 1-13 作者: Liu YP(刘云鹏); Luo HB(罗海波); Li HX(李海星) 收藏 \| 浏览/下载：31/0 \| 提交时间：2019/08/04 paraspinal muscles segmentation U-Net residual module FPA module

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