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241-250 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：

Scale Federated Learning for Label Set Mismatch in medical image Classification 1

引用

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

作者： Deng, Zhipeng Luo, Luyang Chen, Hao Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage. However, most previous studies have assumed that every client holds an identical label set. In reality, medical specialists tend to annotate only diseases within their area of expertise or interest. this implies that label sets in each client can be different and even disjoint. In this paper, we propose the framework FedLSM to solve the problem of Label Set Mismatch. FedLSM adopts different training strategies on data with different uncertainty levels to efficiently utilize unlabeled or partially labeled data as well as class-wise adaptive aggregation in the classification layer to avoid inaccurate aggregation when clients have missing labels. We evaluated FedLSM on two public real-world medical image datasets, including chest X-ray (CXR) diagnosis with 112,120 CXR images and skin lesion diagnosis with 10,015 dermoscopy images, and showed that it significantly out-performed other state-of-the-art FL algorithms. the code can be found at https://***/dzp2095/FedLSM.

关键词： Federated Learning Label Set Mismatch

来源：评论

学校读者我要写书评

暂无评论

Self-supervised Learning via Inter-modal Reconstruction and Feature Projection Networks for Label-Efficient 3D-to-2D Segmentation 26th

Self-supervised Learning via Inter-modal Reconstruction and ...

引用

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

作者： Morano, Jose Aresta, Guilherme Lachinov, Dmitrii Mai, Julia Schmidt-Erfurth, Ursula Bogunovic, Hrvoje Med Univ Vienna Dept Ophthalmol & Optometry Christian Doppler Lab Artificial Intelligence Ret Vienna Austria Med Univ Vienna Dept Ophthalmol & Optometry Lab Ophthalm Image Anal Vienna Austria

ISBN: (纸本)9783031439001;9783031439018

Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D -> 2D. However, the performance of existing methods is strongly conditioned by the amount of labeled data available, as there is currently no data efficient method, e.g. transfer learning, that has been validated on these tasks. In this work, we propose a novel convolutional neural network (CNN) and self-supervised learning (SSL) method for label-efficient 3D -> 2D segmentation. the CNN is composed of a 3D encoder and a 2D decoder connected by novel 3D -> 2D blocks. the SSL method consists of reconstructing image pairs of modalities with different dimensionality. the approach has been validated in two tasks with clinical relevance: the en-face segmentation of geographic atrophy and reticular pseudodrusen in optical coherence tomography. Results on different datasets demonstrate that the proposed CNN significantly improves the state of the art in scenarios with limited labeled data by up to 8% in Dice score. Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.

关键词： image segmentation self-supervised learning OCT retina

来源：评论

学校读者我要写书评

暂无评论

Nonuniformly Spaced Control Points Based on Variational Cardiac image Registration 26th

Nonuniformly Spaced Control Points Based on Variational Card...

引用

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

作者： Su, Haosheng Yang, Xuan Shenzhen Univ Shenzhen 518060 Guangdong Peoples R China Guangdong Prov Key Lab Popular High Performance C Shenzhen Guangdong Peoples R China Shenzhen Key Lab Serv Comp & Applicat Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Non-uniformly spaced control points located on the interface of different objects are beneficial for constructing an accurate displacement field for image registration. However, extracting features of non-uniformly spaced control points in images is challenging for convolutional neural networks (CNNs). We extend a probabilistic image registration model using uniformed-spaced control points by employing non-uniformly-spaced control points. We construct a network to extract the image and spatial features of non-uniformly-spaced control points. Moreover, a variational Bayesian (VB) model using a factorized prior is employed to estimate the distribution of latent variables. In theory, we analyze the KL divergence between the posterior and the two separated priors. We found that the factorized prior has the advantage of decreasing the KL divergence, but too more factorized priors, such as the standard normal, might deteriorate registration accuracy. Moreover, we analyze the relationship between the uncertainty of the displacement field and the spatial distribution of control points. Experimental results on four public datasets show that our network outperforms the state-of-arts registration networks and can provide registration uncertainty.

关键词： Cardiac image registration Variational Bayesian Non-uniformly-spaced control points

来源：评论

学校读者我要写书评

暂无评论

Deblurring Masked Autoencoder Is Better Recipe for Ultrasound image Recognition 26th

Deblurring Masked Autoencoder Is Better Recipe for Ultrasoun...

引用

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

作者： Kang, Qingbo Gao, Jun Li, Kang Lao, Qicheng Sichuan Univ West China Hosp West China Biomed Big Data Ctr Chengdu Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representations that can be transferred to downstream tasks. However, MAE has not been thoroughly explored in ultrasound imaging. In this work, we investigate the potential of MAE for ultrasound image recognition. Motivated by the unique property of ultrasound imaging in high noise-to-signal ratio, we propose a novel deblurring MAE approach that incorporates deblurring into the proxy task during pretraining. the addition of deblurring facilitates the pretraining to better recover the subtle details presented in the ultrasound images, thus improving the performance of the downstream classification task. Our experimental results demonstrate the effectiveness of our deblurring MAE, achieving state-of-the-art performance in ultrasound image classification. Overall, our work highlights the potential of MAE for ultrasound image recognition and presents a novel approach that incorporates deblurring to further improve its effectiveness.

关键词： image Deblurring Masked Autoencoders Self-Supervised Learning Ultrasound Recognition

来源：评论

学校读者我要写书评

暂无评论

ModeT: Learning Deformable image Registration via Motion Decomposition Transformer 26th

ModeT: Learning Deformable Image Registration via Motion Dec...

引用

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

作者： Wang, Haiqiao Ni, Dong Wang, Yi Shenzhen Univ Smart Med Imaging Learning & Engn SMILE Lab Med UltraSound Image Comp MUSIC Lab Sch Biomed EngnMed Sch Shenzhen Peoples R China

ISBN: (纸本)9783031439988;9783031439995

the Transformer structures have been widely used in computer vision and have recently made an impact in the area of medical image registration. However, the use of Transformer in most registration networks is straightforward. these networks often merely use the attention mechanism to boost the feature learning as the segmentation networks do, but do not sufficiently design to be adapted for the registration task. In this paper, we propose a novel motion decomposition Transformer (ModeT) to explicitly model multiple motion modalities by fully exploiting the intrinsic capability of the Transformer structure for deformation estimation. the proposed ModeT naturally transforms the multi-head neighborhood attention relationship into the multi-coordinate relationship to model multiple motion modes. then the competitive weighting module (CWM) fuses multiple deformation sub-fields to generate the resulting deformation field. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets show that our method outperforms current state-of-the-art registration networks and Transformers, demonstrating the potential of our ModeT for the challenging non-rigid deformation estimation problem. the benchmarks and our code are publicly available at https://***/ZAX130/SmileCode.

关键词： Deformable image registration Motion decomposition Transformer Attention Pyramid structure

来源：评论

学校读者我要写书评

暂无评论

PIViT: Large Deformation image Registration with Pyramid-Iterative Vision Transformer 26th

PIViT: Large Deformation Image Registration with Pyramid-Ite...

引用

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

作者： Ma, Tai Dai, Xinru Zhang, Suwei Wen, Ying East China Normal Univ Sch Commun & Elect Engn Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Large deformation image registration is a challenging task in medical image registration. Iterative registration and pyramid registration are two common CNN-based methods for the task. However, these methods usually consume more parameters and time. Additionally, the existing CNN-based registration methods mainly focus on local feature extraction, limiting their ability to capture the long-distance correlation between image pairs. In this paper, we propose a fast and accurate learning-based algorithm, Pyramid-Iterative Vision Transformer (PIViT), for 3D large deformation medical image registration. Our method constructs a novel pyramid iterative composite structure to solve large deformation problem by using low-scale iterative registration with a Swin Transformer-based long-distance correlation decoder. Furthermore, we exploit pyramid structure to supplement the detailed information of the deformation field by using high-scale feature maps. Comprehensive experimental results implemented on brain MRI and liver CT datasets show that the proposed method is superior to the existing registration methods in terms of registration accuracy, training time and parameters, especially of a significant advantage in running time. Our code is available at https://***/Torbjorn1997/PIViT.

关键词： medical image registration convolutional neural networks image processing

来源：评论

学校读者我要写书评

暂无评论

Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps 26th

Flexible Unfolding of Circular Structures for Rendering Text...

引用

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

作者： Rist, Leonhard Taubmann, Oliver Ditt, Hendrik Suhling, Michael Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany Siemens Healthineers CT R&D Image Analyt Forchheim Germany

ISBN: (纸本)9783031439032;9783031439049

Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease, e.g., for fast assessment of vascular topology and lumen in stroke patients. Unfolding the brain vasculature into a 2D overview is, however, infeasible using common Curved Planar Reformation (CPR) due to the circular structure of the Circle of Willis (CoW) and the spatial configuration of the vessels typically rendering them unsuitable for mapping onto simple geometric primitives. We propose CeVasMap, extending the As-Rigid-As-Possible (ARAP) deformation by a smart initialization of the required mesh to map the CoW as well as a merging of neighboring vessels depending on the resulting degree of distortion. Otherwise, vessels are unfolded and attached individually, creating a textbook-style overview image. We provide an extensive distortion analysis, comparing the vector fields of individual and merged unfoldings of each vessel to their CPR results. In addition to enabling unfolding of circular structures, our method is on par in terms of incurred distortions to optimally oriented CPRs for individual vessels and comparable to unfavorable CPR orientations when merging the complete CoW with a median distortion of 65 mu m/mm.

关键词： Vessel unfolding Distortion Cerebrovascular disease

来源：评论

学校读者我要写书评

暂无评论

Correlation-Aware Mutual Learning for Semi-supervised medical image Segmentation 26th

Correlation-Aware Mutual Learning for Semi-supervised Medica...

引用

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

作者： Gao, Shengbo Zhang, Ziji Ma, Jiechao Li, Zihao Zhang, Shu Deepwise AI Lab Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Semi-supervised learning has become increasingly popular in medical image segmentation due to its ability to leverage large amounts of unlabeled data to extract additional information. However, most existing semi-supervised segmentation methods only focus on extracting information from unlabeled data, disregarding the potential of labeled data to further improve the performance of the model. In this paper, we propose a novel Correlation Aware Mutual Learning (CAML) framework that leverages labeled data to guide the extraction of information from unlabeled data. Our approach is based on a mutual learning strategy that incorporates two modules: the Cross-sample Mutual Attention Module (CMA) and the Omni-Correlation Consistency Module (OCC). the CMA module establishes dense cross-sample correlations among a group of samples, enabling the transfer of label prior knowledge to unlabeled data. the OCC module constructs omni-correlations between the unlabeled and labeled datasets and regularizes dual models by constraining the omni-correlation matrix of each sub-model to be consistent. Experiments on the Atrial Segmentation Challenge dataset demonstrate that our proposed approach outperforms state-of-the-art methods, highlighting the effectiveness of our framework in medical image segmentation tasks. the codes, pre-trained weights, and data are publicly available.

关键词： Semi-supervised learning medical image Segmentation Mutual learning Cross-sample correlation

来源：评论

学校读者我要写书评

暂无评论

A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy images 26th

A General Stitching Solution for Whole-Brain 3D Nuclei Insta...

引用

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

作者： Wei, Ziquan Dan, Tingting Ding, Jiaqi Dere, Mustafa Wu, Guorong Univ N Carolina Dept Psychiat Chapel Hill NC 27599 USA Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (纸本)9783031439001;9783031439018

High-throughput 3D nuclei instance segmentation (NIS) is critical to understanding the complex structure and function of individual cells and their interactions within the larger tissue environment in the brain. Despite the significant progress in achieving accurate NIS within small image stacks using cutting-edge machine learning techniques, there has been a lack of effort to extend this approach towards whole-brain NIS from light-sheet microscopy. this critical area of research has been largely overlooked, despite its importance in the neuroscience field. To address this challenge, we propose an efficient deep stitching neural network built upon a knowledge graph model characterizing 3D contextual relationships between nuclei. Our deep stitching model is designed to be agnostic, enabling existing limited methods (optimized for image stack only) to overcome the challenges of whole-brain NIS, particularly in addressing the issue of inter- and intra-slice gaps. We have evaluated the NIS accuracy on top of three state-of-the-art deep models with 128x128x64 image stacks, and visualized results in both inter- and intra-slice gaps of whole brain. With resolved gap issues, our deep stitchingmodel enables the whole-brain NIS (gigapixel-level) on entry-level GPU servers within 27 h.

关键词： image stitching 3D microscopy image Whole-brain nucleus instance segmentation Graph neural network

来源：评论

学校读者我要写书评

暂无评论

COLosSAL: A Benchmark for Cold-Start Active Learning for 3D medical image Segmentation 26th

COLosSAL: A Benchmark for Cold-Start Active Learning for 3D ...

引用

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

作者： Liu, Han Li, Hao Yao, Xing Fan, Yubo Hu, Dewei Dawant, Benoit M. Nath, Vishwesh Xu, Zhoubing Oguz, Ipek Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA NVIDIA Nashville TN USA Siemens Healthineers Princeton NJ USA

ISBN: (纸本)9783031438943;9783031438950

medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the entire data pool is unlabeled, how do we select the samples to annotate as our initial set? this is also known as the cold-start AL, which permits only one chance to request annotations from experts without access to previously annotated data. Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort. In this paper, we present a benchmark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on different strategies. Our results show that cold-start AL is still an unsolved problem for 3D segmentation tasks but some important trends have been observed. the code repository, data partitions, and baseline results for the complete benchmark are publicly available at https://***/MedICL-VU/COLosSAL.

关键词： Efficient Annotation Active Learning Cold Start 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 << < 21 22 23 24 25 26 27 28 29 30 > >>

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：