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291-300 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：

Efficient Registration of Longitudinal Studies for Follow-Up Lesion Assessment by Exploiting Redundancy and Composition of Deformations 26th

Efficient Registration of Longitudinal Studies for Follow-Up...

引用

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

作者： Kuckertz, Sven Heldmann, Stefan Moltz, Jan Hendrik Fraunhofer Inst Digital Med MEVIS Lubeck Germany

ISBN: (纸本)9783031474248;9783031474255

Follow-up assessment of lesions for cancer patients is an important part of radiologists' work. image registration is a key technology to facilitate this task, as it allows for the automatic establishment of correspondences between previous findings and current observations. However, as the number of examinations increases, more registrations must be computed to allow full correspondence assessment between longitudinal studies. We address the challenge of increased computational time and complexity by identifying and eliminating redundant registration procedures and composing deformations from previously performed registrations, thereby significantly reducing the number of registrations required. We evaluate our proposed methods on a dataset consisting of oncological thoracic follow-up CT scans from 260 patients. By grouping series within a study and identifying reference series, we can reduce the total number of registrations required for a patient by an average factor of 27.5 while maintaining comparable registration quality. Additionally composing deformations further reduces the number of registrations by a factor of 1.86, resulting in an overall average reduction factor of 51.4. Since the number of registrations is directly related to the time required to process the input data, the information is availablemore quickly and subsequent examinations can be performed sooner. For a single subject, this results in an exemplary reduction of total computation time from 37.4 to 1.3 min.

关键词： image registration Lesion tracking Follow-up CT

来源：评论

学校读者我要写书评

暂无评论

Non-iterative Coarse-to-Fine Transformer Networks for Joint Affine and Deformable image Registration 26th

Non-iterative Coarse-to-Fine Transformer Networks for Joint ...

引用

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

作者： Meng, Mingyuan Bi, Lei Fulham, Michael Feng, Dagan Kim, Jinman Univ Sydney Sch Comp Sci Sydney NSW Australia Shanghai Jiao Tong Univ Inst Translat Med Shanghai Peoples R China Royal Prince Alfred Hosp Dept Mol Imaging Sydney NSW Australia Shanghai Jiao Tong Univ Med X Res Inst Shanghai Peoples R China

ISBN: (纸本)9783031439988;9783031439995

image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to perform fast end-to-end registration. Many deep registration methods achieved state-of-the-art performance by performing coarse-to-fine registration, where multiple registration steps were iterated with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime. However, existing NICE registrationmethods mainly focus on deformable registration, while affine registration, a common prerequisite, is still reliant on time-consuming traditional optimization-based methods or extra affine registration networks. In addition, existing NICE registration methods are limited by the intrinsic locality of convolution operations. Transformers may address this limitation for their capabilities to capture long-range dependency, but the benefits of using transformers for NICE registration have not been explored. In this study, we propose a Non-Iterative Coarse-to-finE Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the first deep registrationmethod that (i) performs joint affine and deformable coarse-to-fine registration within a single network, and (ii) embeds transformers into a NICE registration framework to model long-range relevance between images. Extensive experiments with seven public datasets show that our NICE-Trans outperforms state-of-the-art registration methods on both registration accuracy and runtime.

关键词： image Registration Coarse-to-fine Registration Transformer

来源：评论

学校读者我要写书评

暂无评论

Regularized Kelvinlet Functions to Model Linear Elasticity for image-to-Physical Registration of the Breast 26th

Regularized Kelvinlet Functions to Model Linear Elasticity f...

引用

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

作者： Ringel, Morgan Heiselman, Jon Richey, Winona Meszoely, Ingrid Miga, Michael Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Mem Sloan Kettering Canc Ctr Dept Surg 1275 York Ave New York NY 10021 USA Vanderbilt Univ Div Surg Oncol Med Ctr Nashville TN USA

ISBN: (纸本)9783031439957;9783031439964

image-guided surgery requires fast and accurate registration to align preoperative imaging and surgical spaces. the breast undergoes large nonrigid deformations during surgery, compromising the use of imaging data for intra-operative tumor localization. Rigid registration fails to account for nonrigid soft tissue deformations, and biomechanical modeling approaches like finite element simulations can be cumbersome in implementation and computation. We introduce regularized Kelvinlet functions, which are closed-form smoothed solutions to the partial differential equations for linear elasticity, to model breast deformations. We derive and present analytical equations to represent nonrigid point-based translation ("grab") and rotation ("twist") deformations embedded within an infinite elastic domain. computing a displacement field using this method does not require mesh discretization or large matrix assembly and inversion conventionally associated with finite element or mesh-free methods. We solve for the optimal superposition of regularized Kelvinlet functions that achieves registration of the medical image to simulated intraoperative geometric point data of the breast. We present registration performance results using a dataset of supineMRbreast imaging from healthy volunteers mimicking surgical deformations with 237 individual targets from 11 breasts. We include analysis on themethod's sensitivity to regularized Kelvinlet function hyperparameters. To demonstrate application, we perform registration on a breast cancer patient case with a segmented tumor and compare performance to other image-to-physical and image-to-image registrationmethods. We show comparable accuracy to a previously proposed image-to-physical registration method with improved computation time, making regularized Kelvinlet functions an attractive approach for image-to-physical registration problems.

关键词： Deformation registration elasticity image-guidance breast finite element Kelvinlet

来源：评论

学校读者我要写书评

暂无评论

DULDA: Dual-Domain Unsupervised Learned Descent Algorithm for PET image Reconstruction 26th

DULDA: Dual-Domain Unsupervised Learned Descent Algorithm fo...

引用

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

作者： Hu, Rui Chen, Yunmei Kim, Kyungsang Rockenbach, Marcio Aloisio Bezerra Cavalcanti Li, Quanzheng Liu, Huafeng Zhejiang Univ Dept Opt Engn State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China Univ Florida Dept Math Gainesville FL 32611 USA Harvard Med Sch Massachusetts Gen Hosp Ctr Adv Med Comp & Anal Boston MA 02114 USA Massachusetts Gen Brigham Data Sci Off Boston MA 02116 USA

ISBN: (纸本)9783031439988;9783031439995

Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstruction method based on learned descent algorithm, which reconstructs high-quality PET images from sinograms without the need for image labels. Specifically, we unroll the proximal gradient method with a learnable l(2,1) norm for PET image reconstruction problem. the training is unsupervised, using measurement domain loss based on deep image prior as well as image domain loss based on rotation equivariance property. the experimental results demonstrate the superior performance of proposed method compared with maximum-likelihood expectation-maximization (MLEM), total-variation regularized EM (EM-TV) and deep image prior based method (DIP).

关键词： image reconstruction Positron emission tomography (PET) Unsupervised learning Model based deep learning Dual-domain

来源：评论

学校读者我要写书评

暂无评论

A Multi-task Method for Immunofixation Electrophoresis image Classification 26th

A Multi-task Method for Immunofixation Electrophoresis Image...

引用

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

作者： Shi, Yi Li, Rui-Xiang Shao, Wen-Qi Duan, Xin-Cen Ye, Han-Jia Zhan, De-Chuan Pan, Bai-Shen Wang, Bei-Li Guo, Wei Jiang, Yuan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Fudan Univ Zhongshan Hosp Dept Lab Med Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

In the field of plasma cell disorders diagnosis, the detection of abnormal monoclonal (M) proteins through Immunofixation Electrophoresis (IFE) is a widely accepted practice. However, the classification of IFE images into nine distinct categories is a complex task due to the significant class imbalance problem. To address this challenge, a two-sub-task classification approach is proposed, which divides the classification task into the determination of severe and mild cases, followed by their combination to produce the final result. this strategy is based on the expert understanding that the nine classes are different combinations of severe and mild cases. Additionally, the examination of the dense band co-location on the electrophoresis lane and other lanes is crucial in the expert evaluation of the image class. To incorporate this expert knowledge into the model training, inner-task and inter-task regularization is introduced. the effectiveness of the proposed method is demonstrated through experiments conducted on approximately 15,000 IFE images, resulting in interpretable visualization outcomes that are in alignment with expert expectations. Codes are available at https://***/shiy19/IFE-classification.

关键词： IFE image classification Multi-task learning Task-related regularization Class imbalance Expert knowledge

来源：评论

学校读者我要写书评

暂无评论

SwinMM: Masked Multi-view with Swin Transformers for 3D medical image Segmentation 1

引用

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

作者： Wang, Yiqing Li, Zihan Mei, Jieru Wei, Zihao Liu, Li Wang, Chen Sang, Shengtian Yuille, Alan L. Xie, Cihang Zhou, Yuyin Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Washington Seattle WA USA Johns Hopkins Univ Baltimore MD USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA Tsinghua Univ Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multiview with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. these tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. this new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://***/UCSC-VLAA/SwinMM/.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

ACC-UNet: A Completely Convolutional UNet Model for the 2020s 1

引用

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

作者： Ibtehaz, Nabil Kihara, Daisuke Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Dept Biol Sci W Lafayette IN 47907 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

this decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision. A similar trend is followed in medical imaging, UNet, one of the most influential architectures, has been redesigned with transformers. Recently, the efficacy of convolutional models in vision is being reinvestigated by seminal works such as ConvNext, which elevates a ResNet to Swin Transformer level. Deriving inspiration from this, we aim to improve a purely convolutional UNet model so that it can be on par with the transformer-based models, e.g., Swin-Unet or UCTransNet. We examined several advantages of the transformer-based UNet models, primarily long-range dependencies and cross-level skip connections. We attempted to emulate them through convolution operations and thus propose, ACC-UNet, a completely convolutional UNet model that brings the best of both worlds, the inherent inductive biases of convnets with the design decisions of transformers. ACC-UNet was evaluated on 5 different medical image segmentation benchmarks and consistently outperformed convnets, transformers, and their hybrids. Notably, ACC-UNet outperforms state-of-the-art models Swin-Unet and UCTransNet by 2.64 +/- 2.54% and 0.45 +/- 1.61% in terms of dice score, respectively, while using a fraction of their parameters (59.26% and 24.24%). Our codes are available at https://***/ kiharalab/ACC- UNet.

关键词： UNet image segmentation fully convolutional network

来源：评论

学校读者我要写书评

暂无评论

A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based medical image Analysis 26th

A Flexible Framework for Simulating and Evaluating Biases in...

引用

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

作者： Stanley, Emma A. M. Wilms, Matthias Forkert, Nils D. Univ Calgary Dept Biomed Engn Calgary AB Canada Univ Calgary Dept Radiol Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Calgary Alberta Childrens Hosp Res Inst Calgary AB Canada Univ Calgary Dept Pediat Calgary AB Canada Univ Calgary Dept Community Hlth Sci Calgary AB Canada Univ Calgary Dept Clin Neurosci Calgary AB Canada

ISBN: (纸本)9783031438943;9783031438950

Despite the remarkable advances in deep learning for medical image analysis, it has become evident that biases in datasets used for training such models pose considerable challenges for a clinical deployment, including fairness and domain generalization issues. Although the development of bias mitigation techniques has become ubiquitous, the nature of inherent and unknown biases in real-world medical image data prevents a comprehensive understanding of algorithmic bias when developing deep learning models and bias mitigation methods. To address this challenge, we propose a modular and customizable framework for bias simulation in synthetic but realistic medical imaging data. Our framework provides complete control and flexibility for simulating a range of bias scenarios that can lead to undesired model performance and short-cut learning. In this work, we demonstrate how this framework can be used to simulate morphological biases in neuroimaging data for disease classification with a convolutional neural network as a first feasibility analysis. Using this case example, we show how the proportion of bias in the disease class and proximity between disease and bias regions can affect model performance and explainability results. the proposed framework provides the opportunity to objectively and comprehensively study how biases in medical image data affect deep learning pipelines, which will facilitate a better understanding of how to responsibly develop models and bias mitigation methods for clinical use. Code is available at ***/estanley16/SimBA.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

UniSeg: A Prompt-Driven Universal Segmentation Model as Well as A Strong Representation Learner 1

引用

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

作者： Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aero Space Ground Ocean Xian 710072 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Northwestern Polytech Univ Ningbo Inst Ningbo 315048 Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build universal models is to encode each task as a one-hot vector and generate dynamic convolutional layers at the end of the decoder to extract the interested target. Although successful, it ignores the correlations among tasks and meanwhile is too late to make the model 'aware' of the ongoing task. To address both issues, we propose a prompt-driven Universal Segmentation model (UniSeg) for multi-task medical image segmentation using diverse modalities and domains. We first devise a learnable universal prompt to describe the correlations among all tasks and then convert this prompt and image features into a task-specific prompt, which is fed to the decoder as a part of its input. thus, we make the model 'aware' of the ongoing task early and boost the task-specific training of the whole decoder. Our results indicate that the proposed UniSeg outperforms other universal models and single-task models on 11 upstream tasks. Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pretrained model for 3D medical image segmentation. Code and model are available at https://***/yeerwen/UniSeg.

关键词： Prompt learning Universal model medical image segmentation

来源：评论

学校读者我要写书评

暂无评论

Learning Robust Classifier for Imbalanced medical image Dataset with Noisy Labels by Minimizing Invariant Risk 26th

Learning Robust Classifier for Imbalanced Medical Image Data...

引用

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

作者： Li, Jinpeng Cao, Hanqun Wang, Jiaze Liu, Furui Dou, Qi Chen, Guangyong Pheng-Ann Heng Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Inst Med Intelligence & XR Shatin Hong Kong Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9783031439865;9783031439872

In medical image analysis, imbalanced noisy dataset classification poses a long-standing and critical problem since clinical large-scale datasets often attain noisy labels and imbalanced distributions through annotation and collection. Current approaches addressing noisy labels and long-tailed distributions separately may negatively impact real-world practices. Additionally, the factor of class hardness hindering label noise removal remains undiscovered, causing a critical necessity for an approach to enhance the classification performance of noisy imbalanced medical datasets with various class hardness. To address this paradox, we propose a robust classifier that trains on a multi-stage noise removal framework, which jointly rectifies the adverse effects of label noise, imbalanced distribution, and class hardness. the proposed noise removal framework consists of multiple phases. Multi-Environment Risk Minimization (MER) strategy captures data-to-label causal features for noise identification, and the Rescaling Class-aware Gaussian Mixture Modeling (RCGM) learns class-invariant detection mappings for noise removal. Extensive experiments on two imbalanced noisy clinical datasets demonstrate the capability and potential of our method for boosting the performance of medical image classification.

关键词： Imbalanced Data Noisy Labels medical 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 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：