机构

• 自动化研究所 [9]
• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
• 遥感与数字地球研究所 [1]
• 沈阳自动化研究所 [1]

采集方式

• OAI收割 [13]

内容类型

• 期刊论文 [7]
• 学位论文 [4]
• 会议论文 [2]

发表日期

• 2024 [1]
• 2023 [2]
• 2022 [2]
• 2021 [2]
• 2014 [1]
• 2012 [2]
• 更多

学科主题

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浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Embedded prompt tuning: Towards enhanced calibration of pretrained models for medical images 期刊论文  OAI收割 MEDICAL IMAGE ANALYSIS, 2024, 卷号: 97, 页码: 13 作者:  Zu, Wenqiang;  Xie, Shenghao;  Zhao, Qing;  Li, Guoqi;  Ma, Lei   |  收藏  |  浏览/下载：8/0  |  提交时间：2024/09/09 Foundation model  Parameter-efficient fine-tuning  Visual prompt tuning  Few-shot medical image analysis   A multi-view co-training network for semi-supervised medical image-based prognostic prediction 期刊论文  OAI收割 NEURAL NETWORKS, 2023, 卷号: 164, 页码: 455-463 作者:  Li, Hailin;  Wang, Siwen;  Liu, Bo;  Fang, Mengjie;  Cao, Runnan   |  收藏  |  浏览/下载：19/0  |  提交时间：2023/11/17 Deep neural network  Medical image analysis  Prognostic prediction  Semi-supervised learning   A Review of Predictive and Contrastive Self-supervised Learning for Medical Images 期刊论文  OAI收割 Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 483-513 作者:  Wei-Chien Wang;  Euijoon Ahn;  Dagan Feng;  Jinman Kim   |  收藏  |  浏览/下载：1/0  |  提交时间：2024/04/23 Self-supervised learning (SSL), contrastive learning, deep learning, medical image analysis, computer vision   A shape-guided deep residual network for automated CT lung segmentation 期刊论文  OAI收割 KNOWLEDGE-BASED SYSTEMS, 2022, 卷号: 250, 页码: 10 作者:  Yang, Lei;  Gu, Yuge;  Huo, Benyan;  Liu, Yanhong;  Bian, Guibin   |  收藏  |  浏览/下载：43/0  |  提交时间：2022/09/19 Deep network architecture  Medical image analysis  Shape stream network  Residual unit  Attention fusion unit   CellNet: A Lightweight Model towards Accurate LOC-Based High-Speed Cell Detection 期刊论文  OAI收割 ELECTRONICS, 2022, 卷号: 11, 期号: 9, 页码: 20 作者:  Long, Xianlei;  Ishii, Idaku;  Gu, Qingyi   |  收藏  |  浏览/下载：38/0  |  提交时间：2022/07/25 cell detection  high-speed vision  convolutional neural network (CNN)  efficient convolutional block  medical image analysis   A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises 期刊论文  OAI收割 PROCEEDINGS OF THE IEEE, 2021, 卷号: 109, 期号: 5, 页码: 820-838 作者:  Zhou, S. Kevin;  Greenspan, Hayit;  Davatzikos, Christos;  Duncan, James S.;  Van Ginneken, Bram   |  收藏  |  浏览/下载：33/0  |  提交时间：2021/12/01 Imaging  Medical diagnostic imaging  Image segmentation  Diseases  Task analysis  Medical services  Computed tomography  Deep learning (DL)  medical imaging  survey   DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2021, 卷号: 16, 期号: 9, 页码: 1425-1434 作者:  Xie, Feng;  Huang Z(黄钲);  Shi, Zhengjin;  Wang TY(王天宇);  Song GL(宋国立)   |  收藏  |  浏览/下载：20/0  |  提交时间：2021/06/18 Medical image analysis  Deep learning  Lesion segmentation  U-Net  Attention mechanism   基于数字几何的脑结构形状分析研究及应用 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院大学, 2014 作者:  陈雪姣 收藏  |  浏览/下载：46/0  |  提交时间：2015/09/02 医学图像  数字几何  脑结构  形状分析  Ricci流  Medical Image  Digital Geometry  Brain Structure  Shape Analysis  Ricci Flow   基于局部特征的医学影像形态分析理论与方法 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2012 作者:  王虎 收藏  |  浏览/下载：40/0  |  提交时间：2015/09/02 医学影像  形态分析  个体分类  局部特征  特征提取  Medical image  morphometric analysis  individual classification  local feature  feature extraction   The research of digltal CR medicine image adapitive enhancement method (EI CONFERENCE) 会议论文  OAI收割 4th International Conference on Mechanical and Electrical Technology, ICMET 2012, July 24, 2012 - July 26, 2012, Kuala Lumpur, Malaysia Ming-Hui Z.; Yao-Yu Z. 收藏  |  浏览/下载：62/0  |  提交时间：2013/03/25 Digital CR medicine radiation image is in doctor's favor and has became medicine imaging technology new hot spot because of its high gray contrast  powerful computer disposal function  little radiation dosage  non-film diagnosis  different area consultation. But degradation of digital X-ray medical image such as low contrast and blurring during radiographic imaging  caused by complexity of body tissue and effects of X-ray scattering and electrical noise etc.  can worsen the results of analysis and diagnosis. So it is usually needed that CR medicine image is enhanced to improve its vision quality  and easy to doctor's more accurate diagnosis. The general enhancement algorithms over enhancing the contrast and lose image details  aiming at the defects  an enhancement algorithm for CR image is proposed based on the ratio of deviation to mean of domain. The arithmetic enhance CR image edge details by adjusting factor K based on the ratio of deviation to mean of domain of CR image. Experiment results demonstrate that the algorithm enhances CR image detail and CR image enhanced has good visual effect  the adaptive enhancement method is fit for CR medicine image. (2012) Trans Tech Publications  Switzerland.