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

2,668 篇 会议
125 篇 期刊文献
89 册 图书

馆藏范围

2,881 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,383 篇 工学
- 2,165 篇 计算机科学与技术...
- 1,123 篇 生物医学工程（可授...
- 934 篇 软件工程
- 549 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
- 1,008 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

330 篇 image segmentati...
127 篇 image registrati...
116 篇 deep learning
110 篇 magnetic resonan...
107 篇 medical imaging
65 篇 image enhancemen...
62 篇 image reconstruc...
60 篇 medical image se...
57 篇 segmentation
57 篇 artificial intel...
56 篇 image processing...
46 篇 pattern recognit...
39 篇 convolutional ne...
38 篇 image classifica...
37 篇 health informati...
35 篇 image analysis
33 篇 computer graphic...
31 篇 deformation
31 篇 computerized tom...
31 篇 image processing

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 univ n carolina ...
12 篇 univ penn dept r...
11 篇 department of co...
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 bai wenjia
17 篇 duncan james s.

语言

2,809 篇 英文
67 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 2882 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：

ConvFormer: Plug-and-Play CNN-Style Transformers for Improving medical image Segmentation 26th

ConvFormer: Plug-and-Play CNN-Style Transformers for Improvi...

引用

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

作者： Lin, Xian Yan, Zengqiang Deng, Xianbo Zheng, Chuansheng Yu, Li Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Huazhong Univ Sci & Technol Union Hosp Dept Radiol Tongji Med Coll Wuhan Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Transformers have been extensively studied in medical image segmentation to build pairwise long-range dependence. Yet, relatively limited well-annotated medical image data makes transformers struggle to extract diverse global features, resulting in attention collapse where attention maps become similar or even identical. Comparatively, convolutional neural networks (CNNs) have better convergence properties on small-scale training data but suffer from limited receptive fields. Existing works are dedicated to exploring the combinations of CNN and transformers while ignoring attention collapse, leaving the potential of transformers under-explored. In this paper, we propose to build CNN-style Transformers (ConvFormer) to promote better attention convergence and thus better segmentation performance. Specifically, ConvFormer consists of pooling, CNN-style self-attention (CSA), and convolutional feed-forward network (CFFN) corresponding to tokenization, self-attention, and feed-forward network in vanilla vision transformers. In contrast to positional embedding and tokenization, ConvFormer adopts 2D convolution and max-pooling for both position information preservation and feature size reduction. In this way, CSA takes 2D feature maps as inputs and establishes long-range dependency by constructing self-attention matrices as convolution kernels with adaptive sizes. Following CSA, 2D convolution is utilized for feature refinement through CFFN. Experimental results on multiple datasets demonstrate the effectiveness of ConvFormer working as a plug-and-play module for consistent performance improvement of transformer-based frameworks. Code is available at https://***/xianlin7/ConvFormer.

关键词： CNN-Style Transformers Attention Collapse Adaptive Self-Attention medical image Segmentation

来源：评论

学校读者我要写书评

暂无评论

VISA-FSS: A Volume-Informed Self Supervised Approach for Few-Shot 3D Segmentation 26th

VISA-FSS: A Volume-Informed Self Supervised Approach for Few...

引用

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

作者： Mozafari, Mohammad Bitarafan, Adeleh Azampour, Mohammad Farid Farshad, Azade Baghshah, Mahdieh Soleymani Navab, Nassir Sharif Univ Technol Tehran Iran Tech Univ Munich Comp Aided Med Proc Munich Germany

ISBN: (纸本)9783031438943;9783031438950

Few-shot segmentation (FSS) models have gained popularity in medical imaging analysis due to their ability to generalize well to unseen classes with only a small amount of annotated data. A key requirement for the success of FSS models is a diverse set of annotated classes as the base training tasks. this is a difficult condition to meet in the medical domain due to the lack of annotations, especially in volumetric images. To tackle this problem, self-supervised FSS methods for 3D images have been introduced. However, existing methods often ignore intra-volume information in 3D image segmentation, which can limit their performance. To address this issue, we propose a novel self-supervised volume-aware FSS framework for 3D medical images, termed VISA-FSS. In general, VISA-FSS aims to learn continuous shape changes that exist among consecutive slices within a volumetric image to improve the performance of 3D medical segmentation. To achieve this goal, we introduce a volume-aware task generation method that utilizes consecutive slices within a 3D image to construct more varied and realistic self-supervised FSS tasks during training. In addition, to provide pseudo-labels for consecutive slices, a novel strategy is proposed that propagates pseudo-labels of a slice to its adjacent slices using flow field vectors to preserve anatomical shape continuity. In the inference time, we then introduce a volumetric segmentation strategy to fully exploit the interslice information within volumetric images. Comprehensive experiments on two common medical benchmarks, including abdomen CT and MRI, demonstrate the effectiveness of our model over state-of-the-art methods. Code is available at https://***/sharif- ml- lab/visa- fss

关键词： medical image segmentation Few-shot learning Few-shot semantic segmentation Self-supervised learning Supervoxels

来源：评论

学校读者我要写书评

暂无评论

Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth image Pairs 26th

Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC St...

引用

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

作者： Li, Fangda Hu, Zhiqiang Chen, Wen Kak, Avinash Purdue Univ W Lafayette IN 47907 USA Sensetime Res Beijing Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory equipment required. this motivates recent research that demonstrates that the correlations between the morphological information present in the H&E-stained slides and the molecular information in the IHC-stained slides can be used for H&E-to-IHC stain translation. However, due to a lack of pixel-perfect H&E-IHC groundtruth pairs, most existing methods have resorted to relying on expert annotations. To remedy this situation, we present a new loss function, Adaptive Supervised PatchNCE (ASP), to directly deal with the input to target inconsistencies in a proposed H&E-to-IHC image-to-image translation framework. the ASP loss is built upon a patch-based contrastive learning criterion, named Supervised PatchNCE (SP), and augments it further with weight scheduling to mitigate the negative impact of noisy supervision. Lastly, we introduce the Multi-IHC Stain Translation (MIST) dataset, which contains aligned H&E-IHC patches for 4 different IHC stains critical to breast cancer diagnosis. In our experiment, we demonstrate that our proposed method outperforms existing image-to-image translation methods for stain translation to multiple IHC stains. All of our code and datasets are available at https://***/lifangda01/AdaptiveSupervisedPatchNCE.

关键词： Generative Adversarial Network Contrastive Learning H&E-to-IHC Stain Translation

来源：评论

学校读者我要写书评

暂无评论

Mathematical Evaluation of medical image computing by Application of Mathematical computer-Aided interventions to Soft Tissues 5

Mathematical Evaluation of Medical Image Computing by Applic...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Chinoy, Danesh Dinyar Mavi, Renu Shrivastav, A.K. Jothi, Naresh Kumar Utsab, Tuhin Rajput, Akshay D y Patil Deemed to Be University School of Physiotherapy Dept of Kinesio Therapeutics and Diagnosis Department of Mathematics Department of Chemistry Navi Mumbai India Swami Vivekananda Subharti University Department of Chemistry Uttar Pradesh Meerut India Mmec Maharshi Markandashwar Department of Mathematics Haryana Ambala India R&d Institute of Science and Technology Department of Mathematics India St. Xavier's University Kolkata India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350383522

Mini-invasive digestive surgery was seemingly one of the main disciplines where soft tissues were handled utilizing test systems, anatomical structure tracking, and specialized assistance robots. Yet, other therapeutic areas, such as urology, are concerned. Unquestionably, laparoscopic surgery, creative growth killing methods (e.g., HIFU, radiofrequency, or cryoablation), prior disease distinguishing proof, and the usage of interventional and diagnostic imaging modalities have of late acquainted new troubles with urologists and researchers keen on CAMI. this has driven in a significant flood in innovative work of computer-aided urology frameworks throughout recent years. In this study, we give a conversation of the main pressing issues related with computer-aided diagnostic and therapy of soft tissues, as well as an overview of the many kinds of help accessible to urologists: robotization, picture combination, and careful route. there is conversation of both exploration projects and functional modern frameworks. © 2024 IEEE.

关键词： computer Aided medical interventions medical image computing

来源：评论

学校读者我要写书评

暂无评论

vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-Level Representations in medical images 26th

vox2vec: A Framework for Self-supervised Contrastive Learnin...

引用

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

作者： Goncharov, Mikhail Soboleva, Vera Kurmukov, Anvar Pisov, Maxim Belyaev, Mikhail Skolkovo Inst Sci & Technol Moscow Russia Artificial Intelligence Res Inst AIRI Moscow Russia Inst Informat Transmiss Problems Moscow Russia IRA Labs Moscow Russia

ISBN: (纸本)9783031439063;9783031439070

this paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations. vox2vec representations are modeled by a Feature Pyramid Network (FPN): a voxel representation is a concatenation of the corresponding feature vectors from different pyramid levels. the FPN is pre-trained to produce similar representations for the same voxel in different augmented contexts and distinctive representations for different voxels. this results in unified multi-scale representations that capture both global semantics (e.g., body part) and local semantics (e.g., different small organs or healthy versus tumor tissue). We use vox2vec to pre-train a FPN on more than 6500 publicly available computed tomography images. We evaluate the pre-trained representations by attaching simple heads on top of them and training the resulting models for 22 segmentation tasks. We show that vox2vec outperforms existing medical imaging SSL techniques in three evaluation setups: linear and non-linear probing and end-to-end fine-tuning. Moreover, a non-linear head trained on top of the frozen vox2vec representations achieves competitive performance with the FPN trained from scratch while having 50 times fewer trainable parameters. the code is available at https://***/mishgon/vox2vec.

关键词： Contrastive Self-Supervised Representation Learning medical image Segmentation

来源：评论

学校读者我要写书评

暂无评论

image2SSM: Reimagining Statistical Shape Models from images with Radial Basis Functions 26th

Image2SSM: Reimagining Statistical Shape Models from Images ...

引用

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

作者： Xu, Hong Elhabian, Shireen Y. Univ Utah Kahlert Sch Comp Sci Comp & Imaging Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9783031439063;9783031439070

Statistical shape modeling (SSM) is an essential tool for analyzing variations in anatomical morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation and rigid registration, are represented using lower-dimensional shape features, on which statistical analysis can be performed. Various methods for constructing compact shape representations have been proposed, but they involve laborious and costly steps. We propose image2SSM, a novel deep-learning-based approach for SSM that leverages image-segmentation pairs to learn a radial-basis-function (RBF)-based representation of shapes directly from images. this RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surface that can adapt to complex geometries in a data-driven manner. image2SSM can characterize populations of biological structures of interest by constructing statistical landmark-based shape models of ensembles of anatomical shapes while requiring minimal parameter tuning and no user assistance. Once trained, image2SSM can be used to infer low-dimensional shape representations from new unsegmented images, paving the way toward scalable approaches for SSM, especially when dealing with large cohorts. Experiments on synthetic and real datasets show the efficacy of the proposed method compared to the state-of-art correspondence-based method for SSM.

关键词： Statistical Shape Modeling Deep Learning Radial Basis Function Interpolation Polyharmonic Splines

来源：评论

学校读者我要写书评

暂无评论

Identifying Shared Neuroanatomic Architecture Between Cognitive Traits through Multiscale Morphometric Correlation Analysis 26th

Identifying Shared Neuroanatomic Architecture Between Cognit...

引用

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

作者： Wen, Zixuan Bao, Jingxuan Yang, Shu Risacher, Shannon L. Saykin, Andrew J. thompson, Paul M. Davatzikos, Christos Huang, Heng Zhao, Yize Shen, Li Univ Penn Philadelphia PA USA Indiana Univ Indianapolis IN USA Univ Southern Calif Los Angels CA USA Univ Maryland College Pk MD USA Yale Univ New Haven CT USA

ISBN: (纸本)9783031474248;9783031474255

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

关键词： Brain image analysis Morphometricity Morphometric Correlation Cognitive Traits Alzheimer's Disease

来源：评论

学校读者我要写书评

暂无评论

Gall Bladder Cancer Detection from US images with only image Level Labels 26th

Gall Bladder Cancer Detection from US Images with only Image...

引用

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

作者： Basu, Soumen Papanai, Ashish Gupta, Mayank Gupta, Pankaj Arora, Chetan Indian Inst Technol Delhi India Postgrad Inst Med Educ & Res Chandigarh India

ISBN: (纸本)9783031439063;9783031439070

Automated detection of Gallbladder Cancer (GBC) from Ultrasound (US) images is an important problem, which has drawn increased interest from researchers. However, most of these works use difficult-to-acquire information such as bounding box annotations or additional US videos. In this paper, we focus on GBC detection using only image-level labels. Such annotation is usually available based on the diagnostic report of a patient, and do not require additional annotation effort from the physicians. However, our analysis reveals that it is difficult to train a standard image classification model for GBC detection. this is due to the low inter-class variance (a malignant region usually occupies only a small portion of a US image), high intra-class variance (due to the US sensor capturing a 2D slice of a 3D object leading to large viewpoint variations), and low training data availability. We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest. Since no bounding box annotations is available for training, we pose the problem as weakly supervised object detection (WSOD). Motivated by the recent success of transformer models in object detection, we train one such model, DETR, using multi-instance-learning (MIL) with self-supervised instance selection to suit the WSOD task. Our proposed method demonstrates an improvement of AP and detection sensitivity over the SOTA transformer-based and CNN-based WSOD methods. Project page is at https://***/wsod-gbc.

关键词： Weakly Supervised Object Detection Ultrasound Gallbladder Cancer

来源：评论

学校读者我要写书评

暂无评论

Towards Frugal Unsupervised Detection of Subtle Abnormalities in medical Imaging 1

引用

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

作者： Oudoumanessah, Geoffroy Lartizien, Carole Dojat, Michel Forbes, Florence Univ Grenoble Alpes CNRS INRIA Grenoble INPLJK F-38000 Grenoble France Univ Grenoble Alpes Grenoble Inst Neurosci Inserm U1216 CHU Grenoble Alpes F-38000 Grenoble France Univ Lyon CNRS INSERM INSA LyonUCBLCREATISUMR5220U1294 F-69621 Villeurbanne France

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. this problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-off between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design effort or tuning. their expressivity makes them good candidates to account for complex multivariate reference models. their much smaller number of parameters makes them more amenable to interpretation and efficient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. this online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. the identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.

关键词： Frugal computing Online EM algorithm Gaussian scale mixture Unsupervised anomaly detection Parkinson's Disease

来源：评论

学校读者我要写书评

暂无评论

5th international conference on Soft computing and its Engineering Applications, icSoftComp 2023

5th International Conference on Soft Computing and its Engin...

引用

5th international conference on Soft computing and its Engineering Applications, icSoftComp 2023

ISBN: (纸本)9783031537301

the proceedings contain 44 papers. the special focus in this conference is on Soft computing and its Engineering Applications. the topics include: Iterative Interference Cancellation for STBC-OFDM System Over Doubly Selective Channel;managing Multiple Identities of IoT Devices Using Blockchain;machine Learning Enabled image Classification Using K-Nearest Neighbour and Learning Vector Quantization;Enhancing IDC Histopathology image Classification: A Comparative Study of Fine-Tuned and Pre-trained Models;optimized Path Planning Techniques for Navigational Control of Mobile Robot Using Grass Fire Algorithm in Obstacle Environment;a Novel Approach for Suggestions on Law Based Problems;performance Evaluation of Service Broker Policies in Cloud computing Environment Using Round Robin;a Course Study Planning Framework – An Engineering Perspective;three Languages Simulate Polygons and Perform on the Web Python, Fortran, C, with PHP and ‘gnuplot’;multi Disease Prediction Using Ensembling of Distinct Machine Learning and Deep Learning Classifiers;Energy-Efficient Task Scheduling in Fog Environment Using TOPSIS;resource Management through Workload Prediction Using Deep Learning in Fog-Cloud Architecture;dynamic Underload Host Detection for Performance Enhancement in Cloud Environment;Inverse Reinforcement Learning to Enhance Physical Layer Security in 6G RIS-assisted Connected Cars;computer Vision-Based Cybersecurity threat Detection System with GAN-Enhanced Data Augmentation;classification Using Optimal Polarimetric Parameters for Compact Polarimetric Data;Comparative Study of Supervised Classification for LULC Using Geospatial Technology;a U-Net Based Approach for High-Accuracy Land Use Land Cover Classification in Hyperspectral Remote Sensing.

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289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：