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

2,658 篇 会议
127 篇 期刊文献
89 册 图书

馆藏范围

2,873 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,384 篇 工学
- 2,166 篇 计算机科学与技术...
- 1,124 篇 生物医学工程（可授...
- 935 篇 软件工程
- 550 篇 光学工程
- 298 篇 生物工程
- 268 篇 电气工程
- 226 篇 电子科学与技术（可...
- 183 篇 信息与通信工程
- 164 篇 控制科学与工程
- 84 篇 仪器科学与技术
- 42 篇 核科学与技术
- 30 篇 力学（可授工学、理...
- 28 篇 化学工程与技术
- 24 篇 安全科学与工程
- 23 篇 机械工程
- 23 篇 动力工程及工程热...
- 20 篇 建筑学
1,834 篇 医学
- 1,359 篇 特种医学
- 1,009 篇 临床医学
- 402 篇 基础医学(可授医学...
- 299 篇 药学(可授医学、理...
- 209 篇 医学技术（可授医学...
- 30 篇 公共卫生与预防医...
692 篇 理学
- 355 篇 数学
- 354 篇 生物学
- 329 篇 物理学
- 111 篇 统计学（可授理学、...
- 45 篇 化学
108 篇 管理学
- 66 篇 图书情报与档案管...
- 44 篇 管理科学与工程(可...
9 篇 教育学
7 篇 农学
4 篇 经济学
2 篇 法学

主题

324 篇 image segmentati...
127 篇 image registrati...
110 篇 deep learning
110 篇 magnetic resonan...
109 篇 medical imaging
65 篇 image enhancemen...
62 篇 image reconstruc...
59 篇 medical image se...
57 篇 segmentation
57 篇 artificial intel...
56 篇 image processing...
46 篇 pattern recognit...
37 篇 health informati...
37 篇 image classifica...
36 篇 image analysis
34 篇 convolutional ne...
33 篇 computer graphic...
33 篇 image processing
31 篇 deformation
31 篇 computerized tom...

机构

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

作者

78 篇 rueckert daniel
61 篇 shen dinggang
50 篇 navab nassir
49 篇 hamarneh ghassan
46 篇 ourselin sebasti...
41 篇 yang guang-zhong
24 篇 glocker ben
23 篇 ni dong
22 篇 wang qian
21 篇 hawkes david j.
21 篇 alexander daniel...
21 篇 davatzikos chris...
19 篇 golland polina
19 篇 abugharbieh rafe...
19 篇 sato yoshinobu
19 篇 fu huazhu
18 篇 yang xin
18 篇 stoyanov danail
18 篇 langs georg
18 篇 bai wenjia

语言

2,820 篇 英文
49 篇 其他
6 篇 中文

检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2874 条记录，以下是181-190 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：

Maximum Entropy on Erroneous Predictions: Improving Model Calibration for medical image Segmentation 1

引用

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

作者： Larrazabal, Agostina J. Martinez, Cesar Dolz, Jose Ferrante, Enzo UNL CONICET Res Inst Signals Syst & Computat Intelligence SinciFICH Santa Fe Argentina ETS Montreal LIVIA Montreal PQ Canada Tryolabs Montevideo Uruguay

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Modern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimates, even in situations of high uncertainty, leading to poorly calibrated and unreliable models. In this work we introduce Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for segmentation networks which selectively penalizes overconfident predictions, focusing only on misclassified pixels. Our method is agnostic to the neural architecture, does not increase model complexity and can be coupled with multiple segmentation loss functions. We benchmark the proposed strategy in two challenging segmentation tasks: white matter hyperintensity lesions in magnetic resonance images (MRI) of the brain, and atrial segmentation in cardiac MRI. the experimental results demonstrate that coupling MEEP with standard segmentation losses leads to improvements not only in terms of model calibration, but also in segmentation quality.

关键词： image segmentation uncertainty calibration

来源：评论

学校读者我要写书评

暂无评论

MRIS: A Multi-modal Retrieval Approach for image Synthesis on Diverse Modalities 26th

MRIS: A Multi-modal Retrieval Approach for Image Synthesis o...

引用

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

作者： Chen, Boqi Niethammer, Marc Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (纸本)9783031439988;9783031439995

Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can be used;for larger changes, direct image synthesis approaches have been explored. In this paper, we develop an approach, MRIS, based on multi-modal metric learning to synthesize images of diverse modalities. We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities. Given a large image database, the learned image embeddings allow us to use k-nearest neighbor (k-NN) regression for image synthesis. Our driving medical problem is knee osteoarthritis (KOA), but our developed method is general after proper image alignment. We test our approach by synthesizing cartilage thickness maps obtained from 3D magnetic resonance (MR) images using 2D radiographs. Our experiments show that the proposed method outperforms direct image synthesis and that the synthesized thickness maps retain information relevant to downstream tasks such as progression prediction and Kellgren-Lawrence grading (KLG). Our results suggest that retrieval approaches can be used to obtain high-quality and meaningful image synthesis results given large image databases. Our code is available at https://***/uncbiag/MRIS.

关键词： Metric learning k-nearest neighbor osteoarthritis

来源：评论

学校读者我要写书评

暂无评论

Fine Grain image classification using Fine-tuned MobileNet model 5

Fine Grain Image classification using Fine-tuned MobileNet m...

引用

5th IEEE international conference for Emerging Technology, INCET 2024

作者： Anvitha, Sagala Sai Reddy, Sagam Prashamsa Sunaini, Yerramachu Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350361155

the paper focuses on the development of an image classifier, which classifies variousfine-grained floral images based on their species. Classification of flowers has many advantages in various fields like Science, medical, Industries *** classifying various types of flowers, one can study their characteristics, etiquette, considering to our knowledge of ecosystems and plant life. Our focal point rests upon the image features woven into the pixels. through various Convolutional neural network models, the work is able to propose an efficient model based on fine-tuned MobileNetv3 architecture and sel-attention mechanismsthat classifies fine-grained flower images into their respective species. © 2024 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

PMC-CLIP: Contrastive Language-image Pre-training Using Biomedical Documents 26th

PMC-CLIP: Contrastive Language-Image Pre-training Using Biom...

引用

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

作者： Lin, Weixiong Zhao, Ziheng Zhang, Xiaoman Wu, Chaoyi Zhang, Ya Wang, Yanfeng Xie, Weidi Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439926;9783031439933

Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before, PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP outperform previous state-of-the-art models on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, medical VQA, for example, +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.

关键词： Multimodal Dataset Vision-Language Pretraining

来源：评论

学校读者我要写书评

暂无评论

Self-adaptive Adversarial Training for Robust medical Segmentation 1

引用

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

作者： Wang, Fu Fu, Zeyu Zhang, Yanghao Ruan, Wenjie Univ Exeter Exeter EX4 4QF Devon England Univ Liverpool Liverpool L69 3BX Merseyside England

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Adversarial training has been demonstrated to be one of the most effective approaches to training deep neural networks that are robust to malicious perturbations. Research on effectively applying it to produce robust 3D medical image segmentation models is ongoing. While few empirical studies have been done in this area, developing effective adversarial training methods for complex segmentation models and high-volume 3D examples is challenging and requires theoretical support. In this paper, we consider the robustness of 3D segmentation tasks from a PAC-Bayes generalisation perceptive and show that reducing the trained models' Lipschitz constant benefits the models' robustness performance. Demonstrating by empirical investigation, we show that adjusting the adversarial iteration can help to reduce the model's Lipschitz constant, enabling a self-adaptive adversarial training strategy. Empirical studies on the medical segmentation decathlon dataset have been done to demonstrate the efficiency of the proposed adversarial training method. Our implementation is available at https://***/TrustAI/SEAT.

关键词： medical image Segmentation Adversarial Training

来源：评论

学校读者我要写书评

暂无评论

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as a Plug-and-Play Transductive Model for medical image Analysis 26th

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Zhang, Yizhe Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031474002;9783031474019

In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image analysis (MIA). We provide theoretical analysis on why a large pre-trained language model such as GPT-3 can be used as a plug-and-play transductive inference model for MIA. At the methodological level, we develop several technical treatments to improve the efficiency and effectiveness of GPT4MIA, including better prompt structure design, sample selection, and prompt ordering of representative samples/features. We present two concrete use cases (with workflow) of GPT4MIA: (1) detecting prediction errors and (2) improving prediction accuracy, working in conjecture with well-established vision-based models for image classification (e.g., ResNet). Experiments validate that our proposed method is effective for these two tasks. We further discuss the opportunities and challenges in utilizing Transformer-based large language models for broader MIA applications.

关键词： medical image Classification Generative Pre-trained Transformer GPT-3 Large Language Models Transductive Inference

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

Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging 26th

Empirical Analysis of a Segmentation Foundation Model in Pro...

引用

26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Kim, Heejong Butoi, Victor Ion Dalca, Adrian V. Sabuncu, Mert R. Weill Cornell Med Dept Radiol New York NY 10065 USA Cornell Univ Sch Elect & Comp Engn Ithaca NY USA Cornell Tech Ithaca NY USA Massachusetts Gen Hosp Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Boston MA USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA

ISBN: (纸本)9783031474002;9783031474019

Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. these models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation have demonstrated the feasibility and utility of building foundation models that can be customized for various downstream tasks with little to no labeled data. this likely represents a paradigm shift for medical imaging, where we expect that foundation models may shape the future of the field. In this paper, we consider a recently developed foundation model for medical image segmentation, UniverSeg [6]. We conduct an empirical evaluation study in the context of prostate imaging and compare it against the conventional approach of training a task-specific segmentation model. Our results and discussion highlight several important factors that will likely be important in the development and adoption of foundation models for medical image segmentation.

关键词： Foundation model medical image Segmentation Prostate MRI In-context Learning

来源：评论

学校读者我要写书评

暂无评论

Anti-adversarial Consistency Regularization for Data Augmentation: Applications to Robust medical image Segmentation 26th

Anti-adversarial Consistency Regularization for Data Augment...

引用

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

作者： Cho, Hyuna Han, Yubin Kim, Won Hwa Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9783031439001;9783031439018

Modern deep learning methods for semantic segmentation require labor-intensive labeling for large-scale datasets with dense pixel-level annotations. Recent data augmentation methods such as dropping, mixing image patches, and adding random noises suggest effective ways to address the labeling issues for natural images. However, they can only be restrictively applied to medical image segmentation as they carry risks of distorting or ignoring the underlying clinical information of local regions of interest in an image. In this paper, we propose a novel data augmentation method for medical image segmentation without losing the semantics of the key objects (e.g., polyps). this is achieved by perturbing the objects with quasi-imperceptible adversarial noises and training a network to expand discriminative regions with a guide of anti-adversarial noises. Such guidance can be realized by a consistency regularization between the two contrasting data, and the strength of regularization is automatically and adaptively controlled considering their prediction uncertainty. Our proposed method significantly outperforms various existing methods with high sensitivity and Dice scores and extensive experiment results with multiple backbones on two datasets validate its effectiveness.

关键词： Adversarial attack and defense Data augmentation Semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Text-Guided Foundation Model Adaptation for Pathological image Classification 26th

Text-Guided Foundation Model Adaptation for Pathological Ima...

引用

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

作者： Zhang, Yunkun Gao, Jin Zhou, Mu Wang, Xiaosong Qiao, Yu Zhang, Shaoting Wang, Dequan Shanghai Jiao Tong Univ Shanghai Peoples R China Rutgers State Univ Newark NJ USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9783031439032;9783031439049

the recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with strong generalizability. Yet pathological image datasets often lack biomedical text annotation and enrichment. Guiding data-efficient image diagnosis from the use of biomedical text knowledge becomes a substantial interest. In this paper, we propose to Connect image and Text Embeddings (CITE) to enhance pathological image classification. CITE injects text insights gained from language models pre-trained with a broad range of biomedical texts, leading to adapt foundation models towards pathological image understanding. through extensive experiments on the PatchGastric stomach tumor pathological image dataset, we demonstrate that CITE achieves leading performance compared with various baselines especially when training data is scarce. CITE offers insights into leveraging in-domain text knowledge to reinforce data-efficient pathological image classification. Code is available at https://***/Yunkun-Zhang/CITE.

关键词： Foundation models Multi-modality Model Adaptation Pathological image classification

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288页 << < 15 16 17 18 19 20 21 22 23 24 > >>

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：