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

2,486 篇 会议
123 篇 期刊文献
85 册 图书

馆藏范围

2,693 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,277 篇 工学
- 2,072 篇 计算机科学与技术...
- 1,061 篇 生物医学工程（可授...
- 847 篇 软件工程
- 506 篇 光学工程
- 263 篇 生物工程
- 238 篇 电气工程
- 191 篇 电子科学与技术（可...
- 157 篇 信息与通信工程
- 145 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
- 18 篇 建筑学
1,767 篇 医学
- 1,334 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...
643 篇 理学
- 341 篇 数学
- 320 篇 生物学
- 288 篇 物理学
- 106 篇 统计学（可授理学、...
- 33 篇 化学
- 16 篇 系统科学
92 篇 管理学
- 62 篇 图书情报与档案管...
- 32 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

302 篇 image segmentati...
118 篇 image registrati...
106 篇 medical imaging
97 篇 magnetic resonan...
91 篇 deep learning
62 篇 image enhancemen...
60 篇 medical image se...
58 篇 image reconstruc...
57 篇 segmentation
55 篇 artificial intel...
53 篇 image processing...
43 篇 pattern recognit...
35 篇 health informati...
35 篇 image analysis
34 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 department of co...
12 篇 univ n carolina ...
12 篇 univ penn dept r...
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

2,670 篇 英文
19 篇 其他
6 篇 中文

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

共 2694 条记录，以下是331-340 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：

Diabetic Retinopathy Detection from Retina image Using Machine Learning 8

Diabetic Retinopathy Detection from Retina Image Using Machi...

引用

8th IEEE international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Manasa, G.R. Anchan, Anusha Santhosh, T. Hemashree, H.C. Pinnapati, Surekha Shetty, Suvith Suresh NMAM Institute of Technology NITTE Deemed to be University Nitte Karkala India Bapuji Institute of Engineering & Technolgy Visvesvaraya technological university Davanagere India Bapuji Institute of Engineering & Technology Vivesvaraya technological university Davangere India STJ Institute of Technology Vivesvaraya technological university Ranebennur India

ISBN: (纸本)9798350350593

Diabetic Retinopathy is a severe complication of diabetes mellitus and one of the primary causes of blindness on a global scale. Early detection and timely intervention are crucial for preventing irreversible vision loss in affected individuals. this paper focuses on the development of an efficient Diabetic Retinopathy (DR) detection system utilizing machine learning techniques. Leveraging a dataset of high-resolution retina images, the system employs preprocessing methods such as image enhancement and normalization, followed by feature extraction to identify distinctive patterns within the images. Different machine learning algorithms, such as Support Vector Machine (SVM), logistic regression, random forest, and Naive Bayes (NB), undergo training and optimization to categorize the progression of Diabetic Retinopathy (DR) across its mild to severe stages. Additionally, explainable AI techniques are integrated to provide insights into the decision-making process of the models, enhancing transparency and trust in the system's predictions. Moreover, the proposed DR detection system offers a non-invasive, cost-effective, and scalable solution for early diagnosis, facilitating timely medical intervention and preventing the progression of diabetic retinopathy. the paper's integration with Gardio interface enables seamless output for new images, further enhancing its usability. this paper underscores the potential of machine learning in revolutionizing healthcare by contributing to the field of medical image analysis. It aims to improve diagnostic capabilities and, ultimately, enhance patient outcomes. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

End-to-End Segmentation of medical images via Patch-Wise Polygons Prediction 25th

End-to-End Segmentation of Medical Images via Patch-Wise Pol...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9783031164439;9783031164422

the leading medical image segmentation methods represent the output map as a pixel grid. We present an alternative in which the object edges are modeled, per image patch, as a polygon with k vertices that is coupled with per-patch label probabilities. the vertices are optimized by employing a differentiable neural renderer to create a raster image. the delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results for the Gland segmentation dataset (Glas), the Nucleus challenges (MoNuSeg), and multiple polyp segmentation datasets, as well as for non-medical benchmarks, including Cityscapes, CUB, and Vaihingen. Our code for training and reproducing these results is attached as a supplement.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Position-Aware Masked Autoencoder for Histopathology WSI Representation Learning 26th

Position-Aware Masked Autoencoder for Histopathology WSI Rep...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Wu, Kun Zheng, Yushan Shi, Jun Xie, Fengying Jiang, Zhiguo Beihang Univ Sch Astronaut Image Proc Ctr Beijing 102206 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Engn Med Beijing 100191 Peoples R China Hefei Univ Technol Sch Software Hefei 230601 Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Transformer-based multiple instance learning (MIL) framework has been proven advanced for whole slide image (WSI) analysis. However, existing spatial embedding strategies in Transformer can only represent fixed structural information, which are hard to tackle the scale-varying and isotropic characteristics of WSIs. Moreover, the current MIL cannot take advantage of a large number of unlabeled WSIs for training. In this paper, we propose a novel self-supervised whole slide image representation learning framework named position-aware masked autoencoder (PAMA), which can make full use of abundant unlabeled WSIs to improve the discrimination of slide features. Moreover, we propose a position-aware cross-attention (PACA) module with a kernel reorientation (KRO) strategy, which makes PAMA able to maintain spatial integrity and semantic enrichment during the training. We evaluated the proposed method on a public TCGA-Lung dataset with 3,064 WSIs and an in-house Endometrial dataset with 3,654 WSIs, and compared it with 6 state-of-the-art methods. the results of experiments show our PAMA is superior to SOTA MIL methods and SSL methods. the code will be available at https://***/WkEEn/PAMA.

关键词： WSI representation learning Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Context-Aware Pseudo-label Refinement for Source-Free Domain Adaptive Fundus image Segmentation 26th

Context-Aware Pseudo-label Refinement for Source-Free Domain...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Huai, Zheang Ding, Xinpeng Li, Yi Li, Xiaomeng Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031439896;9783031439902

In the domain adaptation problem, source data may be unavailable to the target client side due to privacy or intellectual property issues. Source-free unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the source side to align the target distribution with only the source model and unlabeled target data. the source model usually produces noisy and context-inconsistent pseudo-labels on the target domain, i.e., neighbouring regions that have a similar visual appearance are annotated with different pseudo-labels. this observation motivates us to refine pseudo-labels with context relations. Another observation is that features of the same class tend to form a cluster despite the domain gap, which implies context relations can be readily calculated from feature distances. To this end, we propose a context-aware pseudo-label refinement method for SF-UDA. Specifically, a context-similarity learning module is developed to learn context relations. Next, pseudo-label revision is designed utilizing the learned context relations. Further, we propose calibrating the revised pseudo-labels to compensate for wrong revision caused by inaccurate context relations. Additionally, we adopt a pixel-level and class-level denoising scheme to select reliable pseudo-labels for domain adaptation. Experiments on cross-domain fundus images indicate that our approach yields the state-of-the-art results. Code is available at https://***/xmed-lab/CPR.

关键词： Source-free domain adaptation Context similarity Pseudo-label refinement Fundus image

来源：评论

学校读者我要写书评

暂无评论

Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models 3rd

Head CT Scan Motion Artifact Correction via Diffusion-Based ...

引用

3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Chen, Zhennong Yoon, Siyeop Strotzer, Quirin Khalid, Rehab Naeem Tivnan, Matthew Li, Quanzheng Gupta, Rajiv Wu, Dufan Center for Advanced Medical Computing and Analysis Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States

ISBN: (纸本)9783031820069

Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. image-domain-based motion correction is practical for routine use since it doesn’t rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts. Motivated by the improved image quality and more stable training of diffusion-based generative models, we propose a novel 3D head CT motion correction approach based on conditional diffusion, named HeadMotion-EDM (HM-EDM). this approach has three features. Firstly, we utilize motion-corrupted images as the conditional input. Secondly, we leverage the advanced Elucidated Diffusion Model (EDM) framework, which integrates several pivotal engineering improvements in the diffusion model and significantly expedites the sampling process. thirdly, we design an efficient 3D-patch-wise training method for 3D CT data. Comparative studies demonstrate that our approach surpasses CNN-based techniques as well as the denoising diffusion probabilistic model (DDPM) in both simulation and phantom studies. Furthermore, radiologists reviewed the results of applying HM-EDM to real-world portable head CT scans, showing its effectiveness in eliminating motion artifacts and improving diagnostic value. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Groupwise image Registration with Atlas of Multiple Resolutions Refined at Test Phase 26th

Groupwise Image Registration with Atlas of Multiple Resoluti...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： He, Ziyi Mok, Tony C. W. Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China

ISBN: (纸本)9783031474248;9783031474255

Groupwise image registration (GIR) is a fundamental task that facilitates the simultaneous deformation of a group of subjects towards a specified or implicit center. Existing works mainly focus on either optimization-based methods that provide superb results but consume substantial time, or learning-based methods that are efficient but lack the flexibility to generalize across different domains and scales. To leverage the advantages of both methodologies, we present a robust method, Test-time Atlas adaptation for Groupwise registration (TAG), which generates a high-quality, group-specific atlas for groups of varying resolutions. Our method allows training at the test phase on target groups based on a learning-based GIR framework that bridges the gap between diverse groups. Besides the refinement of atlases at the original resolution, we propose additional modules to extend the scheme to groups of higher or lower resolutions at little cost. the method is evaluated on 3D brain MRI datasets to demonstrate its effectiveness. Evaluations of the registration accuracy and unbiasedness of atlases illustrate that TAG outperforms state-of-the-art benchmarks and maintains flexibility and robustness under a variety of scenarios.

关键词： Atlas Generation Test-time Adapation Multiscale Imaging

来源：评论

学校读者我要写书评

暂无评论

Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning 26th

Encoding Surgical Videos as Latent Spatiotemporal Graphs for...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Murali, Aditya Alapatt, Deepak Mascagni, Pietro Vardazaryan, Armine Garcia, Alain Okamoto, Nariaki Mutter, Didier Padoy, Nicolas Univ Strasbourg CNRS ICube Strasbourg France IHU Strasbourg Inst Image Guided Surg Strasbourg France Fdn Policlin Univ Agostino Gemelli IRCCS Rome Italy Inst Res Digest Canc IRCAD Strasbourg France

ISBN: (纸本)9783031439957;9783031439964

Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream tasks such as action recognition. In this work, we investigate the use of latent spatiotemporal graphs to represent a surgical video in terms of the constituent anatomical structures and tools and their evolving properties over time. To build the graphs, we first predict frame-wise graphs using a pre-trained model, then add temporal edges between nodes based on spatial coherence and visual and semantic similarity. Unlike previous approaches, we incorporate long-term temporal edges in our graphs to better model the evolution of the surgical scene and increase robustness to temporary occlusions. We also introduce a novel graph-editing module that incorporates prior knowledge and temporal coherence to correct errors in the graph, enabling improved downstream task performance. Using our graph representations, we evaluate two downstream tasks, critical view of safety prediction and surgical phase recognition, obtaining strong results that demonstrate the quality and flexibility of the learned representations. Code is available at ***/CAMMA-public/SurgLatentGraph.

关键词： Scene Graphs Surgical Scene Understanding Representation Learning

来源：评论

学校读者我要写书评

暂无评论

IIB-MIL: Integrated Instance-Level and Bag-Level Multiple Instances Learning with Label Disambiguation for Pathological image Analysis 26th

IIB-MIL: Integrated Instance-Level and Bag-Level Multiple In...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Ren, Qin Zhao, Yu He, Bing Wu, Bingzhe Mai, Sijie Xu, Fan Huang, Yueshan He, Yonghong Huang, Junzhou Yao, Jianhua Tencent AI Lab Shenzhen 518000 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China ShanghaiTech Univ Shanghai 201210 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9783031439865;9783031439872

Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and diverse nature of whole-slide images (WSIs), analyzing them through multiple instance learning (MIL) has become a widely-used scheme, which, however, faces the challenges that come with the weakly supervised nature of MIL. Conventional MIL methods mostly either utilized instance-level or bag-level supervision to learn informative representations from WSIs for downstream tasks. In this work, we propose a novel MIL method for pathological image analysis with integrated instance-level and bag-level supervision (termed IIB-MIL). More importantly, to overcome the weakly supervised nature of MIL, we design a label-disambiguation-based instance-level supervision for MIL using Prototypes and Confidence Bank to reduce the impact of noisy labels. Extensive experiments demonstrate that IIB-MIL outperforms state-of-the-art approaches in both benchmarking datasets and addressing the challenging practical clinical task. the code is available at https://***/TencentAILabHealthcare/IIB-MIL.

关键词： computational pathology multi-instance learning label disambiguation prototype confidence bank

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Anonymization of Chest Radiographs: A Utility-Preserving Measure for Patient Privacy 1

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Packhaeuser, Kai Guendel, Sebastian thamm, Florian Denzinger, Felix Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Robust and reliable anonymization of chest radiographs constitutes an essential step before publishing large datasets of such for research purposes. the conventional anonymization process is carried out by obscuring personal information in the images with black boxes and removing or replacing meta-information. However, such simple measures retain biometric information in the chest radiographs, allowing patients to be re-identified by a linkage attack. therefore, there is an urgent need to obfuscate the biometric information appearing in the images. We propose the first deep learning-based approach (PriCheXy-Net) to targetedly anonymize chest radiographs while maintaining data utility for diagnostic andmachine learning purposes. Ourmodel architecture is a composition of three independent neural networks that, when collectively used, allow for learning a deformation field that is able to impede patient re-identification. Quantitative results on the ChestX-ray14 dataset show a reduction of patient re-identification from 81.8% to 57.7% (AUC) after re-training with little impact on the abnormality classification performance. this indicates the ability to preserve underlying abnormality patterns while increasing patient privacy. Lastly, we compare our proposed anonymization approach with two other obfuscation-based methods (Privacy-Net, DP-Pix) and demonstrate the superiority of our method towards resolving the privacy-utility trade-off for chest radiographs.

关键词： image Anonymization Patient Privacy Data Utility

来源：评论

学校读者我要写书评

暂无评论

Identification of Disease-Sensitive Brain Imaging Phenotypes and Genetic Factors Using GWAS Summary Statistics 26th

Identification of Disease-Sensitive Brain Imaging Phenotypes...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Xi, Duo Cui, Dingnan Zhang, Jin Shang, Muheng Zhang, Minjianan Guo, Lei Han, Junwei Du, Lei Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9783031439032;9783031439049

Brain imaging genetics is a rapidly growing neuroscience area that integrates genetic variations and brain imaging phenotypes to investigate the genetic underpinnings of brain disorders. In this field, using multi-modal imaging data can leverage complementary information and thus stands a chance of identifying comprehensive genetic risk factors. Due to privacy and copyright issues, many imaging and genetic data are unavailable, and thus existing imaging genetic methods cannot work. In this paper, we proposed a novel multi-modal brain imaging genetic learning method that can study the associations between imaging phenotypes and genetic variations using genome-wide association study (GWAS) summary statistics. Our method leverages the powerful multi-modal of brain imaging phenotypes and GWAS. More importantly, it does not need to access the imaging and genetic data of each individual. Experimental results on both Alzheimer's Disease Neuroimaging Initiative (ADNI) database and GWAS summary statistics suggested that our method has the same learning ability, including identifying associations between genetic biomarkers and imaging phenotypes and selecting relevant biomarkers, as those counterparts depending on the individual data. therefore, our learning method provides a novel methodology for brain imaging genetics without individual data.

关键词： Brain imaging genetics GWAS summary statistics Multi-modal brain image analysis

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 << < 30 31 32 33 34 35 36 37 38 39 > >>

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：