中国科学院机构知识库网格系统: 检索

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	Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning 期刊论文 OAI收割 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2024, 卷号: 28 作者: Li, Qiankun; Huang, Xiaolong; Fang, Bo; Chen, Huabao; Ding, Siyuan \| 收藏 \| 浏览/下载：8/0 \| 提交时间：2024/11/20 Large natural data medical image cross-domain learning staged fine-tuning Large natural data medical image cross-domain learning staged fine-tuning
	Pre-training in Medical Data: A Survey 期刊论文 OAI收割 Machine Intelligence Research, 2023, 卷号: 20, 期号: 2, 页码: 147-149 作者: Yixuan Qiu; Feng Lin; Weitong Chen; Miao Xu \| 收藏 \| 浏览/下载：6/0 \| 提交时间：2024/04/23 Medical data pre-training transfer learning self-supervised learning medical image data electrocardiograms (ECG) data
	MinimalGAN: diverse medical image synthesis for data augmentation using minimal training data 期刊论文 OAI收割 APPLIED INTELLIGENCE, 2022 作者: Zhang, Yipeng; Wang, Quan; Hu, Bingliang \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2022/07/12 Image generation Data augmentation Image segmentation Medical imaging
	基于改进U-Net的关节滑膜磁共振图像的分割期刊论文 OAI收割计算机应用, 2020, 卷号: 40, 期号: 11, 页码: 3340 作者: 魏小娜; 邢嘉祺; 王振宇; 王颖珊; 石洁 \| 收藏 \| 浏览/下载：6/0 \| 提交时间：2024/05/20 synovitis magnetic resonance image medical image segmentation data augmentation U-Net 滑膜炎磁共振图像医学图像分割数据增广 U-Net
	Applying maximally stable extremal regions and local binary patterns for guide-wire detecting in percutaneous coronary intervention 期刊论文 OAI收割 IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 13, 页码: 2579-2586 作者: Pusit, Prasong; Xie, Xiao-Liang; Hou, Zeng-Guang \| 收藏 \| 浏览/下载：51/0 \| 提交时间：2020/03/30 blood vessels medical image processing surgery image sequences video signal processing image filtering object detection X-ray imaging object tracking stroke width variation filter region detection local binary patterns guide-wire recognition conventional MSER methods maximally stable extremal regions guide-wire position anatomical skeleton contours training data X-ray video sequence percutaneous coronary intervention surgery region area range filter X-ray video monitoring guide-wire tip detection modified multifilters training templates
	The dynamic micro computed tomography at SSRF 期刊论文 OAI收割 JOURNAL OF INSTRUMENTATION, 2018, 卷号: 13 作者: Chen, R.; Xu L(许良); Du, G.; Deng, B.; Xie, H. \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2018/07/03 Computerized Tomography (Ct) And Computed Radiography (Cr) Image Reconstruction In Medical imagIng Inspection With X-rays Data Compression
	Web-based Multi-dimensional Medical Image Collaborative Annotation System 会议论文 OAI收割 international conference on information technology and software engineering, beijing, china, 2012-12-8 作者: Gaihong Yu; Dianfu Ma; Hualei Shen; Yonggang Huang 收藏 \| 浏览/下载：24/0 \| 提交时间：2016/10/18 Medical image Annotation Data model Collaborative
	The compression and storage method of the same kind of medical images-DPCM (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 Medical imaging has started to take advantage of digital technology opening the way for advanced medical imaging and teleradiology. Medical images however require large amounts of memory. At over 1 million bytes per image a typical hospital needs a staggering amount of memory storage (over one trillion bytes per year) and transmitting an image over a network (even the promised superhighway) could take minutes - too slow for interactive teleradiology. This calls for image compression to reduce significantly the amount of data needed to represent an image. Several compression techniques with different compression ratio have been developed. However the lossless techniques which allow for perfect reconstruction of the original images yield modest compression ratio while the techniques that yield higher compression ratio are lossy that is the original image is reconstructed only approximately Medical imaging poses the great challenge of having compression algorithms that are lossless (for diagnostic and legal reasons) and yet have high compression ratio for reduced storage and transmission time. To meet this challenge we are developing and studying some compression schemes which are either strictly lossless or diagnostically lossless taking advantage of the peculiarities of medical images and of the medical practice. In order to increase the Signal to-Noise Ratio (SNR) by exploitation of correlations within the source signal a method of combining differential pulse code modulation (DPCM) is presented.
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：42/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.