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

3,922 篇 会议
108 篇 期刊文献
80 册 图书

馆藏范围

4,109 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,628 篇 工学
- 3,283 篇 计算机科学与技术...
- 1,893 篇 生物医学工程（可授...
- 1,553 篇 软件工程
- 992 篇 光学工程
- 501 篇 生物工程
- 379 篇 电气工程
- 319 篇 电子科学与技术（可...
- 269 篇 信息与通信工程
- 201 篇 控制科学与工程
- 157 篇 仪器科学与技术
- 63 篇 核科学与技术
- 45 篇 力学（可授工学、理...
- 37 篇 安全科学与工程
- 36 篇 化学工程与技术
- 34 篇 机械工程
- 28 篇 建筑学
2,707 篇 医学
- 1,923 篇 特种医学
- 1,682 篇 临床医学
- 666 篇 基础医学(可授医学...
- 466 篇 药学(可授医学、理...
- 322 篇 医学技术（可授医学...
- 62 篇 公共卫生与预防医...
1,105 篇 理学
- 573 篇 生物学
- 516 篇 物理学
- 482 篇 数学
- 143 篇 统计学（可授理学、...
- 60 篇 化学
141 篇 管理学
- 94 篇 图书情报与档案管...
- 50 篇 管理科学与工程(可...
30 篇 教育学
- 30 篇 教育学
9 篇 农学
7 篇 经济学
6 篇 法学
1 篇 军事学

主题

334 篇 image segmentati...
183 篇 magnetic resonan...
181 篇 deep learning
161 篇 medical imaging
129 篇 image registrati...
84 篇 image enhancemen...
79 篇 segmentation
76 篇 image reconstruc...
71 篇 medical image se...
68 篇 computerized tom...
66 篇 artificial intel...
59 篇 image processing...
54 篇 deformation
52 篇 pattern recognit...
49 篇 image classifica...
48 篇 convolutional ne...
46 篇 magnetic resonan...
45 篇 semi-supervised ...
42 篇 contrastive lear...
41 篇 image analysis

机构

31 篇 ucl ctr med imag...
21 篇 queens univ sch ...
21 篇 imperial coll lo...
20 篇 shenzhen univ ma...
19 篇 kings coll londo...
19 篇 simon fraser uni...
19 篇 shenzhen univ me...
17 篇 ucl ctr med imag...
17 篇 chinese univ hon...
16 篇 tencent jarvis l...
15 篇 centre for medic...
15 篇 shanghai united ...
15 篇 department of co...
15 篇 univ n carolina ...
14 篇 national center ...
14 篇 shanghai jiao to...
14 篇 harvard med sch ...
13 篇 biomedical image...
13 篇 univ penn dept r...
13 篇 shanghaitech uni...

作者

93 篇 rueckert daniel
76 篇 navab nassir
71 篇 shen dinggang
50 篇 ourselin sebasti...
48 篇 hamarneh ghassan
44 篇 yang guang-zhong
33 篇 zheng yefeng
31 篇 ni dong
29 篇 duncan james s.
29 篇 zhou s. kevin
28 篇 glocker ben
26 篇 yang xin
25 篇 comaniciu dorin
25 篇 abugharbieh rafe...
25 篇 zheng guoyan
24 篇 yap pew-thian
24 篇 fichtinger gabor
23 篇 golland polina
23 篇 abolmaesumi pura...
22 篇 collins d. louis

语言

4,057 篇 英文
50 篇 其他
8 篇 中文

检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是81-90 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：

1st international Challenge on Semi-supervised Teeth Segmentation, STS 2023, held in conjunction with the international conference on medical image computing and computer assisted intervention, MICCAI 2023

1st International Challenge on Semi-supervised Teeth Segment...

引用

1st international Challenge on Semi-supervised Teeth Segmentation, STS 2023, held in conjunction with the international conference on medical image computing and computer assisted intervention, MICCAI 2023

ISBN: (纸本)9783031723957

The proceedings contain 16 papers. The special focus in this conference is on Semi-supervised Teeth Segmentation. The topics include: Perform Special Post-processing After Tooth Segmentation;A Multi-stage Framework for 3D Individual Tooth Segmentation in Dental CBCT;preprocessing of Prior Knowledge Before Semi-supervised Tooth Segmentation;a Semi-supervised Tooth Segmentation Method Based on Entropy-Guided Mean Teacher and Weakly Mutual Consistency Network;msNet: Multi-stage Learning from Seldom Labeled Data for 3D Tooth Segmentation in Dental Cone Beam Computed Tomography;Diffusion-Based Conv-Former Dual-Encode U-Net: DDPM for Level Set Evolution Mapping MICCAI STS 2023 Challenge;Semi-supervised 3D Tooth Segmentation Using nn-UNet with Axial Attention and Positional Correction;boundary Feature Fusion Network for Tooth image Segmentation;self-training Based Semi-Supervised Learning and U-Net with Denoiser for Teeth Segmentation in X-Ray image;UX-CNet: Effective Edge Information Acquisition for Teeth image Segmentation;2D Teeth Segmentation Base on Half-image Approach and VCMix-Net+;Automated Dental CBCT Segmentation Using Pseudo Labeling Method;prior-Aware Cross Pseudo Supervision for Semi-supervised Tooth Segmentation;High-Precision Semi-supervised 3D Dental Segmentation Based on nnUNet.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust T-Loss for medical image Segmentation 1

引用

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

作者： Gonzalez-Jimenez, Alvaro Lionetti, Simone Gottfrois, Philippe Groeger, Fabian Pouly, Marc Navarini, Alexander A. Univ Basel Basel Switzerland Lucerne Sch Comp Sci & Informat Technol Rotkreuz Switzerland

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter. This parameter is updated during the backpropagation process, eliminating the need for additional computation or prior information about the level and spread of noisy labels. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets for skin lesion and lung segmentation. We also demonstrate the ability of T-Loss to handle different types of simulated label noise, resembling human error. Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. The project website can be found at https:// robust- ***.

关键词： robust loss medical image segmentation noisy labels

来源：评论

学校读者我要写书评

暂无评论

Large Scale image Data Processing and Content Recognition Based on Deep Learning 3

Large Scale Image Data Processing and Content Recognition Ba...

引用

3rd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Wan, Ruoyan Wei, Xiuzhuo Ma, Rui Zhao, Huinan Wu, Feng Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

As the cornerstone of computer vision (CV), image recognition is of great importance. It not only profoundly affects daily life fields such as facial recognition, intelligent security, and medical image analysis, but also plays a key role in breakthroughs in high-tech fields such as autonomous driving technology, intelligent robots, and industrial inspection. With the vigorous rise of artificial intelligence (AI) technology, especially the rapid development of deep learning (DL) technology, image recognition has ushered in unprecedented changes. This article focuses on the latest applications of DL in the field of image recognition and proposes an innovative large-scale image data processing and content recognition algorithm. This algorithm effectively captures complex features in images by constructing deep neural network models, and continuously optimizes model parameters using massive training data, thereby improving recognition accuracy and significantly enhancing the algorithm's ability to understand image content. The experimental results show that the algorithm exhibits excellent performance in processing large-scale image datasets, with fast recognition speed and industry-leading recognition accuracy, laying a solid foundation for the further application of image recognition technology. © 2024 IEEE.

关键词： medical robotics

来源：评论

学校读者我要写书评

暂无评论

Brain Stroke Detection using CT images 3

Brain Stroke Detection using CT Images

引用

3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Kanchana, M. Shankar, R. Harihharann, G. Department of computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

It is through stroke that disability and mortality are caused in most populations worldwide;therefore, fast detection and accuracy for timely intervention are required. This project is developing an advanced brain stroke detection system based on a combination of medical imaging and machine learning algorithms. It uses data from the CT scan and applies image processing to extract features such as ischemic areas, hemorrhagic regions, and perfusion deficits. The project is designed to train machine learning models, specifically deep learning architectures such as CNNs, for automatic classification of stroke types and severity, including ischemic and hemorrhagic, with a view to detecting sensitivity and accuracy as well as specificity and specificity, which will be used to validate the reliability of the model. This project directly points to a more efficient diagnosis of strokes by healthcare professionals, less delay in commencing treatment, and improved patient outcomes. Its solution allows integration into hospital systems for the better diagnostic capabilities in cities and outside but without neurologists' access. © 2024 IEEE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

medical image Classification Using Decision Trees 3

Medical Image Classification Using Decision Trees

引用

3rd Odisha international conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2024

作者： Aruna Devi, B. Kannan, M. Rajesh Amrita Viswa Vidyapeetham Kerala Hut Lab Ece Dept. India

ISBN: (纸本)9798350354379

computer aided Diagnosis (CAD) has been usually applied in MR images to detect tumour and treat diseases. This work is classifying automated system to differentiate the MR images as affected and not affected quickly. It helps to doctor's analysis and decrease the investigation time. This work is therapeutic correction for the pancreatic tumour classification by performing 3 classifiers such as C4.5, C5.0 and Random Forest. The 161 pancreas MR images are classified as either affected or not affected by using GLCM feature extraction. Among the 3 discrimination methods, C5.0 yields best accuracy (99%) with less error rate. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Assessing the Impact of Sociotechnical Harms in AI-Based medical image Analysis 2nd

Assessing the Impact of Sociotechnical Harms in AI-Based ...

引用

2nd international Workshop on Fairness of AI in medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on medical image computing and computer assisted interventions, MICCAI 2024

作者： Stanley, Emma A. M. Souza, Raissa Winder, Anthony J. Wilms, Matthias Pike, G. Bruce Dagasso, Gabrielle Nielsen, Christopher MacEachern, Sarah J. Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute University of Calgary Calgary Canada Department of Pediatrics University of Calgary Calgary Canada

ISBN: (纸本)9783031727863

Clinical decision-making and radiology will inevitably be transformed by artificial intelligence (AI) in the coming years. The rapid adoption of AI in this domain, and in other everyday applications, has brought an increased awareness of the potential impacts and negative consequences that may occur throughout the sociotechnical systems that these technologies are implemented in. In this paper, we review and apply a previously published taxonomy of the sociotechnical harms of AI to investigate how these harms could manifest during the development and clinical implementation of AI-based medical image analysis. Through an illustrative case study example on computer-aided diagnosis using brain magnetic resonance imaging, we demonstrate how performing impact assessments of sociotechnical harms can assist in operationalizing the medical ethics principle of non-maleficence, thereby guiding the ethical development and implementation of AI technologies in healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： medical imaging

来源：评论

学校读者我要写书评

暂无评论

7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

7th International Workshop on Machine Learning in Clinical N...

引用

7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

ISBN: (纸本)9783031787607

The proceedings contain 16 papers. The special focus in this conference is on Machine Learning in Clinical Neuroimaging. The topics include: Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning;hyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks;SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis;a Lightweight 3D Conditional Diffusion Model for Self-explainable Brain Age Prediction in Adults and Children;SOE: SO(3)-Equivariant 3D MRI Encoding;towards a Foundation Model for Cortical Folding;a Lesion-Aware Edge-Based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia;DISARM: Disentangled Scanner-Free image Generation via Unsupervised image2image Translation;segmenting Small Stroke Lesions with Novel Labeling Strategies;a Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis;Surface-Based Parcellation and Vertex-wise Analysis of Ultra High-resolution ex vivo 7 tesla MRI in Alzheimer’s disease and related dementias;Self-supervised Pre-training Tasks for an fMRI Time-Series Transformer in Autism Detection;is Your Style Transfer Doing Anything Useful? An Investigation into Hippocampus Segmentation and the Role of Preprocessing;GAMing the Brain: Investigating the Cross-Modal Relationships Between Functional Connectivity and Structural Features Using Generalized Additive Models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for medical image Classification 9th

Probing the Efficacy of Federated Parameter-Efficient Fine-T...

引用

27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Alkhunaizi, Naif Almalik, Faris Al-Refai, Rouqaiah Naseer, Muzammal Nandakumar, Karthik Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of data silos, and stringent privacy constraints exacerbate this fine-tuning problem in the medical imaging domain, creating a strong need for algorithms that enable collaborative fine-tuning of pre-trained models. Moreover, the large size of these models necessitates the use of parameter-efficient fine-tuning (PEFT) to reduce the communication burden in federated learning. In this work, we systematically investigate various federated PEFT strategies for adapting a Vision Transformer (ViT) model (pre-trained on a large natural image dataset) for medical image classification. Apart from evaluating known PEFT techniques, we introduce new federated variants of PEFT algorithms such as visual prompt tuning (VPT), low-rank decomposition of visual prompts, stochastic block attention fine-tuning, and hybrid PEFT methods like low-rank adaptation (LoRA)+VPT. Moreover, we perform a thorough empirical analysis to identify the optimal PEFT method for the federated setting and understand the impact of data distribution on federated PEFT, especially for out-of-domain (OOD) and non-IID data. The key insight of this study is that while most federated PEFT methods work well for in-domain transfer, there is a substantial accuracy vs. efficiency trade-off when dealing with OOD and non-IID scenarios, which is commonly the case in medical imaging. Specifically, every order of magnitude reduction in fine-tuned/exchanged parameters can lead to a 4% drop in accuracy. Thus, the choice of the initial model is critical for the effectiveness of federated PEFT - rather than starting with general vision models, it is preferable to use medical foundation models (if available) learned using in-domain medical image data. Code: https://***/Naiftt/PEFT.

关键词： Vision Transformers Parameter-Efficient Fine-tuning Out-of-Domain Transfer Federated Learning

来源：评论

学校读者我要写书评

暂无评论

Partially Supervised Multi-organ Segmentation via Affinity-Aware Consistency Learning and Cross Site Feature Alignment 26th

Partially Supervised Multi-organ Segmentation via Affinity-A...

引用

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

作者： Zhou, Qin Liu, Peng Zheng, Guoyan Shanghai Jiao Tong Univ Inst Med Robot Sch Biomed Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Partially Supervised Multi-Organ Segmentation (PSMOS) has attracted increasing attention. However, facing with challenges from lacking sufficiently labeled data and cross-site data discrepancy, PSMOS remains largely an unsolved problem. In this paper, to fully take advantage of the unlabeled data, we propose to incorporate voxel-to-organ affinity in embedding space into a consistency learning framework, ensuring consistency in both label space and latent feature space. Furthermore, to mitigate the cross-site data discrepancy, we propose to propagate the organ-specific feature centers and inter-organ affinity relationships across different sites, calibrating the multi-site feature distribution from a statistical perspective. Extensive experiments manifest that our method generates favorable results compared with other state-of-the-art methods, especially on hard organs with relatively smaller sizes.

关键词： Multi-organ Segmentation Partially Supervised Affinity Relationship Consistency Learning

来源：评论

学校读者我要写书评

暂无评论

5th international Workshop on Advances in Simplifying medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

5th International Workshop on Advances in Simplifying Medica...

引用

5th international Workshop on Advances in Simplifying medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031736469

The proceedings contain 21 papers. The special focus in this conference is on Advances in Simplifying medical Ultrasound. The topics include: Do High-Performance image-to-image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer;PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement;PIPsUS: Self-supervised Point Tracking in Ultrasound;structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train;an Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy;Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound;automatic Facial Axes Standardization of 3D Fetal Ultrasound images;C-TRUS: A Novel Dataset and Initial Benchmark for Colon Wall Segmentation in Transabdominal Ultrasound;label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets with Domain Shift and Partial Labelling;introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography;interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images;Enhanced Uncertainty Estimation in Ultrasound image Segmentation with MSU-Net;multi-site Class-Incremental Learning with Weighted Experts in Echocardiography;Masked Autoencoders for medical Ultrasound Videos Using ROI-Aware Masking;uncertainty-Based Multi-modal Learning for Myocardial Infarction Diagnosis Using Echocardiography and Electrocardiograms;fetal Ultrasound Video Representation Learning Using Contrastive Rubik’s Cube Recovery;LoRIS - Weakly-Supervised Anomaly Detection for Ultrasound images;unsupervised Detection of Fetal Brain Anomalies Using Denoising Diffusion Models;diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound.

关键词：

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411页 << < 5 6 7 8 9 10 11 12 13 14 > >>

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：