检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

2,484 篇 会议
122 篇 期刊文献
85 册 图书

馆藏范围

2,690 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,271 篇 工学
- 2,063 篇 计算机科学与技术...
- 1,060 篇 生物医学工程（可授...
- 847 篇 软件工程
- 508 篇 光学工程
- 263 篇 生物工程
- 229 篇 电气工程
- 191 篇 电子科学与技术（可...
- 148 篇 信息与通信工程
- 144 篇 控制科学与工程
- 78 篇 仪器科学与技术
- 33 篇 核科学与技术
- 28 篇 力学（可授工学、理...
- 22 篇 机械工程
- 22 篇 化学工程与技术
- 22 篇 安全科学与工程
- 17 篇 建筑学
1,767 篇 医学
- 1,333 篇 特种医学
- 958 篇 临床医学
- 365 篇 基础医学(可授医学...
- 260 篇 药学(可授医学、理...
- 207 篇 医学技术（可授医学...
- 29 篇 公共卫生与预防医...
635 篇 理学
- 332 篇 数学
- 320 篇 生物学
- 289 篇 物理学
- 106 篇 统计学（可授理学、...
- 33 篇 化学
- 16 篇 系统科学
98 篇 管理学
- 63 篇 图书情报与档案管...
- 38 篇 管理科学与工程(可...
11 篇 教育学
7 篇 农学
3 篇 经济学
3 篇 法学

主题

303 篇 image segmentati...
118 篇 image registrati...
105 篇 medical imaging
96 篇 magnetic resonan...
91 篇 deep learning
62 篇 image enhancemen...
60 篇 medical image se...
58 篇 image reconstruc...
57 篇 segmentation
54 篇 artificial intel...
53 篇 image processing...
43 篇 pattern recognit...
35 篇 health informati...
34 篇 image analysis
33 篇 image classifica...
30 篇 deformation
30 篇 computer graphic...
30 篇 computerized tom...
28 篇 semi-supervised ...
28 篇 contrastive lear...

机构

30 篇 ucl ctr med imag...
19 篇 simon fraser uni...
17 篇 ucl ctr med imag...
16 篇 shenzhen univ ma...
16 篇 shenzhen univ me...
14 篇 biomedical image...
14 篇 national center ...
14 篇 univ penn dept r...
14 篇 imperial coll lo...
13 篇 hong kong univ s...
13 篇 chinese univ hon...
12 篇 centre for medic...
12 篇 college of physi...
12 篇 shanghai united ...
12 篇 univ n carolina ...
12 篇 univ penn dept r...
11 篇 department of co...
10 篇 tencent jarvis l...
10 篇 siemens corp res...
9 篇 yale univ dept b...

作者

78 篇 rueckert daniel
61 篇 shen dinggang
51 篇 navab nassir
48 篇 hamarneh ghassan
46 篇 ourselin sebasti...
41 篇 yang guang-zhong
23 篇 ni dong
23 篇 glocker ben
22 篇 wang qian
22 篇 davatzikos chris...
21 篇 alexander daniel...
20 篇 zhou s. kevin
19 篇 golland polina
19 篇 abugharbieh rafe...
19 篇 fu huazhu
19 篇 bai wenjia
18 篇 sato yoshinobu
18 篇 yang xin
17 篇 duncan james s.
17 篇 yap pew-thian

语言

2,667 篇 英文
19 篇 其他
8 篇 中文

检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2691 条记录，以下是751-760 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Hemo-FS-SAM2: Few-Shot Tuning on Hemorrhage Segmentation of Segment Anything Model 2

Hemo-FS-SAM2: Few-Shot Tuning on Hemorrhage Segmentation of ...

引用

Advanced Algorithms and Control Engineering (ICAACE), international conference on

作者： Xi Wang Peng Qiao Yong Dou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Surgical hemorrhage is a common occurrence in surgeries. Accurate segmentation of hemorrhage regions is important for surgical navigation and post-operative assessment. Some segmentation models focused on medical images, but its performance on hemorrhage data is limited. Besides, there is an essential challenge to annotate a large number of hemorrhage data, and previous segmentation methods struggle with complex hemorrhage characteristics like unclear boundaries and scattered targets. the Segment Anything Model 2 (SAM2) shows an significant zero-shot ability in general image segmentation, and is generalizable to fine-tuned on downstream tasks. However, it often faces limitations in hemorrhage segmentation task that lacking annotations. this paper proposes a fine-tuning approach for SAM2, significantly improving its performance on hemorrhage segmentation tasks with limited data. Our method provides better segmentation performance on few-shot hemorrhage data than SAM and SAM2 based models.

关键词： image segmentation Adaptation models Accuracy Annotations Shape Surgery Data models Hemorrhaging Tuning Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Inferior Alveolar Nerve Segmentation in CBCT images Using Connectivity-Based Selective Re-training

Inferior Alveolar Nerve Segmentation in CBCT Images Using C...

引用

2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Liu, Yusheng Xin, Rui Yang, Tao Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031889769

Inferior Alveolar Nerve (IAN) canal detection in CBCT is an important step in many dental and maxillofacial surgery applications to prevent irreversible damage to the nerve during the procedure. the ToothFairy2023 Challenge aims to establish a 3D maxillofacial dataset comprising both sparse and partially dense labels, with the goal of improving the accuracy of automatic IAN segmentation. In this work, in order to avoid the negative impact brought by sparse labeling, we transform the mixed supervised problem into a semi-supervised problem. Inspired by self-training via pseudo labeling, we propose a selective re-training framework based on IAN connectivity. Our method is quantitatively evaluated on the ToothFairy verification cases, achieving the dice similarity coefficient (DSC) of 0.7956, and 95% hausdorff distance (HD95) of 4.4905, and wining the champion in the competition. Code is available at https://***/GaryNico517/SSL-IAN-Retraining. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Assesment of bone mineral density in X-ray images using image processing 8

Assesment of bone mineral density in X-ray images using imag...

引用

8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Dodamani, Pooja S. Danti, Ajit Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9789380544434

X-ray application in medical fields has given rise to various research challenges related to bone, due to its wide usage in finding out the disease related to human anatomy. It has lot of research challenges to solve using available wide application of medical imaging techniques and inspired by this, a novel X-ray images based survey was conducted to understand the role of Xray images in medical field. Bone mass density identification is the standard procedure to monitor the risk of fracture in bone using DEXA. Lot of research has been carried out to calculate BMD using X-ray images and it provided prominent results. Since Xray is economically affordable and very economical compared to DEXA, we have decided to work on X-ray images. this paper explains us about various current advancements and disadvantages with respect to X-ray image in medical sector and various techniques related to BMD calculation. X-ray images characteristics and its fundamentals in the medical field for identifying bone related diseases are also discussed. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Bone

来源：评论

学校读者我要写书评

暂无评论

Scaling nnU-Net for CBCT Segmentation 2nd

Scaling nnU-Net for CBCT Segmentation

引用

作者： Isensee, Fabian Kirchhoff, Yannick Kraemer, Lars Rokuss, Maximilian Ulrich, Constantin Maier-Hein, Klaus H. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031889769

this paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge. We leveraged the nnU-Net ResEnc L model, introducing key modifications to patch size, network topology, and data augmentation strategies to address the unique challenges of dental CBCT imaging. Our method achieved a mean Dice coefficient of 0.9253 and HD95 of 18.472 on the test set, securing a mean rank of 4.6 and with it the first place in the ToothFairy2 challenge. the source code is publicly available, encouraging further research and development in the field. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Patch-Free 3D medical image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance 24th

Patch-Free 3D Medical Image Segmentation Driven by Super-Res...

引用

international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wang, Hongyi Lin, Lanfen Hu, Hongjie Chen, Qingqing Li, Yinhao Iwamoto, Yutaro Han, Xian-Hua Chen, Yen-Wei Tong, Ruofeng Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Sir Run Run Shaw Hosp Dept Radiol Hangzhou Peoples R China Zhejiang Lab Res Ctr Healthcare Data Sci Hangzhou Peoples R China Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Japan Yamaguchi Univ Artificial Intelligence Res Ctr Yamaguchi Japan

ISBN: (纸本)9783030871932;9783030871925

3D medical image segmentation with high resolution is an important issue for accurate diagnosis. the main challenge for this task is its large computational cost and GPU memory restriction. Most of the existing 3D medical image segmentation methods are patch-based methods, which ignore the global context information for accurate segmentation and also reduce the efficiency of inference. To tackle this problem, we propose a patch-free 3D medical image segmentation method, which can realize high-resolution (HR) segmentation with low-resolution (LR) input. It contains a multi-task learning framework (Semantic Segmentation and Super-Resolution (SR)) and a Self-Supervised Guidance Module (SGM). SR is used as an auxiliary task for the main segmentation task to restore the HR details, while the SGM, which uses the original HR image patch as a guidance image, is designed to keep the high-frequency information for accurate segmentation. Besides, we also introduce a Task-Fusion Module (TFM) to exploit the inter connections between the segmentation and SR tasks. Since the SR task and TFM are only used in the training phase, they do not introduce extra computational costs when predicting. We conduct the experiments on two different datasets, and the experimental results show that our framework outperforms current patch-based methods as well as has a 4x higher speed when predicting. Our codes are available at https://***/ Dootmaan/PFSeg.

关键词： 3D medical image segmentation Patch-free Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Robust Zero Watermarking Algorithm for medical images Using Local Binary Pattern and Discrete Cosine Transform 8th

Robust Zero Watermarking Algorithm for Medical Images Using ...

引用

8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Liu, Wenyi Li, Jingbing Shao, Chunyan Ma, Jixin Huang, Mengxing Bhatti, Uzair Aslam School of Information and Communication Engineering Hainan University Haikou570228 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China

ISBN: (纸本)9783031067631

thanks to the rapid development of the telemedicine, medical treatment has turned in great convenience. However, as the internet network can be addressed by any user, the medical images cannot avoid the defects of suffering from information leakage and require information encryption techniques. To protect the privacy of patients in medical images, a robust watermarking method, which combines medical images with watermark information, is proposed in this paper. First, the low-frequency feature vector of the medical image is extracted by combining local binary pattern (LBP) and transformation with discrete cosine transform (DCT). Second, hash sequence is implemented on to convert feature vector into binary sequences. At last, the medical image is embedded with hash sequence and watermark preprocessed by chaotic encryption technology using the zero-watermark technology. the experimental results show that the method can encrypt the medical images’ watermark effectively and has good robustness against geometric attacks. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Lightweight Encryption Scheme for Bio-metric 3-Plane image Encryption Based on L-System Fractal and 2-D Chaotic ACM 8th

Lightweight Encryption Scheme for Bio-metric 3-Plane Image ...

引用

8th international conference on Advances in computing and Data Sciences, ICACDS 2024

作者： Khaladkar, Vrushali Kumar, Manish Maharashtra Pune India Mathematics Department Birla Institute of Technology and Science-Pilani Hyderabad Campus Telangana Hyderabad500078 India

ISBN: (纸本)9783031709050

In the current era, major technology is cloud computing and Internet of things due to their vast applications in day-to-day life. the major reason behind today’s internet traffic is big data transmission and usage in varied data types like textual, image and video datasets, as well as audio data. Due to widely spread usage of social media, security and authenticity of image data is of utmost priority especially when it comes to applications like medical images, Geographic imaging system, biometric authenticity. the major objective of this research is to provide a sustainable robust encryption algorithm for securing images where data authenticity is of priority like biometric images. Algorithm is lightweight cryptographic algorithm as it uses low dimensional chaotic map Arnold’s CAT map (ACM) and L-System fractal for key generation. the results tabulated show satisfactory output towards statistical tests. Robustness and effectiveness of the proposed algorithm is compared decently with other existing algorithms and it demonstrates practical usability of the algorithm. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fractals

来源：评论

学校读者我要写书评

暂无评论

mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network 24th

mfTrans-Net: Quantitative Measurement of Hepatocellular Carc...

引用

international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhao, Jianfeng Xiao, Xiaojiao Li, Dengwang Chong, Jaron Kassam, Zahra Chen, Bo Li, Shuo Western Univ London ON Canada Taiyuan Univ Technol Taiyuan 030000 Shanxi Peoples R China Shandong Normal Univ Sch Phys & Elect Shandong Inst Ind Technol Hlth Sci & Precis Med Shandong Key Lab Med Phys & Image Proc Jinan 250358 Peoples R China London Hlth Sci Ctr London ON Canada Digital Imaging Grp London London ON Canada

ISBN: (纸本)9783030872403;9783030872397

Quantitative measurement of hepatocellular carcinoma (HCC) on multi-phase contrast-enhanced magnetic resonance imaging (CEMRI) is one of the key processes for HCC treatment and prognosis. However, direct automated quantitative measurement using the CNN-based network a still challenging task due to: (1) the lack of ability for capturing long-range dependencies of multi-anatomy in the whole medical image;(2) the lack of mechanism for fusing and selecting multi-phase CEMRI information. In this study, we propose a multi-function Transformer regression network (mfTrans-Net) for HCC quantitative measurement. Specifically, we first design three parallel CNN-based encoders for multi-phase CEMRI feature extraction and dimension reducing. Next, the non-local Transformer makes our mfTrans-Net self-attention for capturing the long-range dependencies of multi-anatomy. At the same time, a phase-aware Transformer captures the relevance between multi-phase CEMRI for multi-phase CEMRI information fusion and selection. Lastly, we proposed a multi-level training strategy, which enables an enhanced loss function to improve the quantification task. the mfTrans-Net is validated on multi-phase CEMRI of 138 HCC subjects. Our mfTrans-Net achieves high performance of multi-index quantification that the mean absolute error of center point, max-diameter, circumference, and area is down to 2.35 mm, 2 38 mm, 8.28 mm, and 116.15 mm(2), respectively. the results show that mfTrans-Net has great potential for small lesions quantification in medical images and clinical application value.

关键词： Quantitative measurement Non-local transformer Phase-aware transformer

来源：评论

学校读者我要写书评

暂无评论

Abnormality Detection in Brain Tissue: A deep Learning Approach

Abnormality Detection in Brain Tissue: A deep Learning Appro...

引用

international conference on Contemporary computing and Informatics (IC3I)

作者： Kiran Preet Bedi Jitendra Singh Jadon Department of Artificial Intelligence Amity School of Engineering and Technology and Technology Amity University Noida Uttar Pradesh India Department of Artificial Intelligence Amity School of Engineering Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Glioblastoma, the most prevalent and aggressive form of brain cancer in adults, presents substantial challenges in both diagnosis and treatment. A crucial prognostic factor and an indicator of chemotherapy responsiveness in this context is the identification of MGMT promoter methylation within the tumour. However, the existing genetic analysis procedures for brain cancer involve invasive methods and time-intensive processes. the primary aim of this initiative is to enhance diagnosis and treatment strategies for glioblastoma patients, with a focus on reducing the reliance on invasive procedures and streamlining the genetic analysis workflow. through the utilization of radio genomics, the goal is to develop a non-invasive approach that predicts the genetic profile of the tumour exclusively through imaging to tackle these challenges, the Radiological Society of North America (RSNA) and the medical image computing and computer assisted intervention Society (MICCAI Society) have collaborated to host a competition focused on glioblastoma diagnosis and treatment planning. the competition revolves around the use of MRI scans to construct a model capable of accurately forecasting the genetic subtype of glioblastoma by identifying the presence of MGMT promoter methylation. the positive outcomes derived from this competition are poised to significantly contribute to less invasive diagnostic procedures and more personalized treatment strategies for individuals battling brain cancer. this abstract provides a comprehensive overview of the project’s objectives, the contextual backdrop of the competition, and the potential transformative impact on the management and survival rates of those affected by glioblastoma.

关键词： Deep learning image segmentation Magnetic resonance imaging Computational modeling Brain cancer Streaming media Predictive models Brain modeling Planning Tumors

来源：评论

学校读者我要写书评

暂无评论

Automatic Multi-structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures 2nd

Automatic Multi-structure Segmentation in Cone Beam Compute...

引用

作者： Wodzinski, Marek Müller, Henning Department of Measurement and Electronics AGH University of Krakow Krakow Poland Information Systems Institute Sierre Switzerland Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031889769

Automatic multi-structure segmentation in cone beam computed tomography is an important area of research in dentistry. Accurate segmentation of structures such as the mandible, maxillary bones, pharynx, and inferior alveolar canal is crucial for all head and neck surgical specialties. the importance of the task motivated the researchers to organize a dedicated challenge, ToothFairy2, aiming to benchmark distinct segmentation methods from researchers from all over the world. In this work, we present our contribution to the challenge, based on a patch-based encoder-decoder segmentation architecture. We compare several building blocks and show that, for this particular task, the most important factor is a data sampling strategy that correctly balances the class distribution. During the challenge, our method achieved a considerable score for the lower jaw structures (avgDSC = 0.878) but did not adequately segment the upper jaw structures due to incorrect case-level sampling (avgDSC = 0.738). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共270页 << < 72 73 74 75 76 77 78 79 80 81 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：