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

2,484 篇 会议
123 篇 期刊文献
85 册 图书

馆藏范围

2,691 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,272 篇 工学
- 2,064 篇 计算机科学与技术...
- 1,061 篇 生物医学工程（可授...
- 848 篇 软件工程
- 509 篇 光学工程
- 263 篇 生物工程
- 229 篇 电气工程
- 191 篇 电子科学与技术（可...
- 148 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...
635 篇 理学
- 332 篇 数学
- 320 篇 生物学
- 289 篇 物理学
- 106 篇 统计学（可授理学、...
- 33 篇 化学
- 16 篇 系统科学
98 篇 管理学
- 63 篇 图书情报与档案管...
- 38 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
54 篇 artificial intel...
53 篇 image processing...
43 篇 pattern recognit...
35 篇 health informati...
35 篇 image analysis
33 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

2,678 篇 英文
9 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 2692 条记录，以下是541-550 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：

Diabetic Foot Ulcer Classification Using Deep Learning Approach 8th

Diabetic Foot Ulcer Classification Using Deep Learning Appro...

引用

8th IFIP TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Sivakamasundari, P. Niranjana, G. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9783031736162

Podiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause "Diabetic Peripheral Neuropathy," a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. the basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. this work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set. © IFIP international Federation for Information Processing 2025.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Poisson2Sparse: Self-supervised Poisson Denoising from a Single image 25th

Poisson2Sparse: Self-supervised Poisson Denoising from a Sin...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Ta, Calvin-Khang Aich, Abhishek Gupta, Akash Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9783031164521;9783031164514

image enhancement approaches often assume that the noise is signal independent, and approximate the degradation model as zero-mean additive Gaussian. However, this assumption does not hold for biomedical imaging systems where sensor-based sources of noise are proportional to signal strengths, and the noise is better represented as a Poisson process. In this work, we explore a sparsity and dictionary learning-based approach and present a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data. Specifically, we approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network that enforces sparsity with respect to the weights of the network. Since the sparse representations are based on the underlying image, it is able to suppress the spurious components (noise) in the image patches, thereby introducing implicit regularization for denoising tasks through the network structure. Experiments on two bio-imaging datasets demonstrate that our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM. Our qualitative results demonstrate that, in addition to higher performance on standard quantitative metrics, we are able to recover much more subtle details than other compared approaches. Our code is made publicly available at https://***/tacalvin/Poisson2Sparse.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Multimodal Brain Tumor Segmentation Using Modified 3D UNet3+ Architecture 1

引用

Challenge on Brain Tumor Segmentation, BraTS 2023, international Challenge on Cross-Modality Domain Adaptation for medical image Segmentation, CrossMoDA 2023, held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2023

作者： Yang, Xiao Zheng, Shaohua Intelligent Image Processing and Analysis Laboratory Fuzhou University Fuzhou China

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Glioma is a type of tumor that originates in the glial cells of the brain or the spinal cord, which is the most common and aggressive malignant primary tumor of the central nervous system in adults. Magnetic resonance imaging (MRI) is an efficient way of detection for brain tumors, it contains precise knowledge about the location of the tumor and its component. Manual tumor labeling is a tedious and time-consuming task, hence accurate automatic segmentation has important clinical significance in the diagnosis, and deep learning shows great power on it. In this paper, we propose an improved 3D U-Net3 + segmentation network. We adopt the residual structure in the encoder part to address the challenge of diminishing gradient problem, as well as to enhance the encoder’s capability for feature extraction, thereby facilitating comprehensive feature fusion during up-sampling within the network. Meanwhile, we add a critic network at the end of the U-Net3+, that contributes to the generation of segmentation results that are more authentic and dependable from the segmentation network. We trained and evaluated the architecture on the BraTS 2023 dataset, and achieved LesionWise_Dice of 88.22%, 83.85%, 81.97% for the whole tumor, tumor core and enhancing tumor, respectively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised medical image Classification with Temporal Knowledge-Aware Regularization 25th

Semi-supervised Medical Image Classification with Temporal K...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Yang, Qiushi Liu, Xinyu Chen, Zhen Ibragimov, Bulat Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ Copenhagen Dept Comp Sci DK-2100 Copenhagen Denmark

ISBN: (纸本)9783031164521;9783031164514

Semi-supervised learning (SSL) for medical image classification has achieved exceptional success on efficiently exploiting knowledge from unlabeled data with limited labeled data. Nevertheless, recent SSL methods suffer from misleading hard-form pseudo labeling, exacerbating the confirmation bias issue due to rough training process. Moreover, the training schemes excessively depend on the quality of generated pseudo labels, which is vulnerable against the inferior ones. In this paper, we propose TEmporal knowledge-Aware Regularization (TEAR) for semisupervised medical image classification. Instead of using hard pseudo labels to train models roughly, we design Adaptive Pseudo Labeling (AdaPL), a mild learning strategy that relaxes hard pseudo labels to soft-form ones and provides a cautious training. AdaPL is built on a novel theoretically derived loss estimator, which approximates the loss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the model to learn discriminative representations in an unsupervised manner. the core principle of IPH is to maintain the harmonization of clustered prototypes across different iterations. Both AdaPL and IPH can be easily incorporated into prior pseudo labeling-based models to extract features from unlabeled medical data for accurate classification. Extensive experiments on three semi-supervised medical image datasets demonstrate that our method outperforms state-of-theart approaches. the code is available at littps://***/City U-AIM-Group/TEAR.

关键词： Semi-supervised learning medical image classification Temporal knowledge Pseudo labeling Prototype harmonizing

来源：评论

学校读者我要写书评

暂无评论

Swin-MMC: Swin-Based Model for Myopic Maculopathy Classification in Fundus images 1

引用

Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

作者： Lu, Li Pan, Xuhao Jin, Panji Ding, Ye School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China School of Statistics and Information Shanghai University of International Business and Economics Shanghai201620 China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic maculopathy is a highly myopic retinal disorder that often occurs in highly myopic patients, serving as a major cause of visual impairment and blindness in numerous countries. Currently, fundus images serve as a prevalent diagnostic tool for myopic maculopathy. However, its efficacy relies on the expertise of clinicians, making the process labor-intensive. thus, we propose a model specifically designed for the image classification of myopic maculopathy, named Swin-MMC, based on the Swin Transformer model architecture, which achieves outstanding performance on the test dataset. To achieve a finer-grained classification of myopic maculopathy in fundus images, we have innovatively and for the first time proposed the use of enhanced ArcFace loss in medical image classification. then, based on the Swin-MMC model, we introduce a weak label strategy that effectively mitigates overfitting. Our approach achieves significantly improved results on the test dataset and can be easily used for various datasets and classification tasks. We conduct a series of experiments in the MMAC2023 challenge. In the testing phase, our average performance metric reaches 86.60%. In the further testing phase, our model’s performance improves to 88.23%, ultimately securing the championship in the MMAC2023 challenge. the codes allowing replication of this study have been made publicly available at https://***/LuliDreamAI/MICCAI_TASK1. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework with Cycle-Consistent Constraints 25th

Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework wi...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhang, Jiadong Cui, Zhiming Jiang, Caiwen Zhang, Jingyang Gao, Fei Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai 200230 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Positron emission tomography (PET) is an important medical imaging technique, especially for brain and cancer disease diagnosis. Modern PET scanner is usually combined with computed tomography (CT), where CT image is used for anatomical localization, PET attenuation correction, and radiotherapy treatment planning Considering radiation dose of CT image as well as increasing spatial resolution of PET image, there is a growing demand to synthesize CT image from PET image (without scanning CT) to reduce risk of radiation exposure. However, most existing works perform learning-based image synthesis to construct cross-modality mapping only in the image domain, without considering of the projection domain, leading to potential physical inconsistency. To address this problem, we propose a novel PET-CT synthesis framework by exploiting dual-domain information (i.e., image domain and projection domain). Specifically, we design both image domain network and projection domain network to jointly learn high-dimensional mapping from PET to CT. the image domain and the projection domain can be connected together with a forward projection (FP) and a filtered back projection (FBP). To further help the PET-to-CT synthesis task, we also design a secondary CT-toPET synthesis task with the same network structure, and combine the two tasks into a bidirectional mapping framework with several closed cycles. More importantly, these cycles can serve as cycle-consistent losses to further help network training for better synthesis performance. Extensive validations on the clinical PET-CT data demonstrate the proposed PET-CT synthesis framework outperforms the state-of-the-art (SOTA) medical image synthesis methods with significant improvements.

关键词： medical image synthesis Dual-domain information Cycle-consistent constraint

来源：评论

学校读者我要写书评

暂无评论

End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation 25th

End-to-End Multi-Slice-to-Volume Concurrent Registration and...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Leroy, Amaury Lerousseau, Marvin Henry, theophraste Cafaro, Alexandre Paragios, Nikos Gregoire, Vincent Deutsch, Eric Therapanacea Paris France Paris Saclay Univ Cent Supelec Gif Sur Yvette France Ctr Leon Berard Dept Radiat Oncol Lyon France Paris Saclay Univ Gustave Roussy Inserm Villejuif France

ISBN: (纸本)9783031164460;9783031164453

For interventional procedures, a real-time mapping between treatment guidance images and planning data is challenging yet essential for successful therapy implementation. Because of time and machine constraints, it involves imaging of different modalities, resolutions and dimensions, along with severe out-of-plane deformations to handle. In this paper, we introduce MSV-RegSyn-Net, a novel, scalable, deep learning-based framework for concurrent slice-to-volume registration and high-resolution modality transfer synthesis. It consists of an end-to-end pipeline made up of (i) a cycle generative adversarial network for multimodal image translation combined with (ii) a multi-slice-to-volume deformable registration network. the concurrent nature of our approach creates mutual benefit for both tasks: image translation is naturally eased by explicit handling of out-of-plane deformations while registration benefits from bringing multimodal signals into the same domain. Our model is fully unsupervised and does not require any ground-truth deformation or segmentation mask. It obtains superior qualitative and quantitative performance for multi-slice MR to 3D CT pelvic imaging compared to state-of-the-art traditional and learning-based methods on both tasks.

关键词： Multimodal registration 2D-3D image-to-image translation Generative adversarial network Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Domain Adaptive Fundus image Segmentation with Category-Level Regularization 25th

Unsupervised Domain Adaptive Fundus Image Segmentation with ...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Feng, Wei Wang, Lin Ju, Lie Zhao, Xin Wang, Xin Shi, Xiaoyu Ge, Zongyuan Monash Univ Monash Med AI Grp Melbourne Australia Monash Univ Airdoc Monash Res Ctr Clayton Vic Australia Monash Univ Monash eResearch Ctr Clayton Vic Australia Airdoc LLC Beijing Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks. However, these methods focus only on global distribution adaptation and ignore distribution constraints at the category level, which would lead to sub-optimal adaptation performance. this paper presents an unsupervised domain adaptation framework based on category-level regularization that regularizes the category distribution from three perspectives. Specifically, for inter-domain category regularization, an adaptive prototype alignment module is proposed to align feature prototypes of the same category in the source and target domains. In addition, for intra-domain category regularization, we tailored a regularization technique for the source and target domains, respectively. In the source domain, a prototype-guided discriminative loss is proposed to learn more discriminative feature representations by enforcing intra-class compactness and inter-class separability, and as a complement to traditional supervised loss. In the target domain, an augmented consistency category regularization loss is proposed to force the model to produce consistent predictions for augmented/unaugmented target images, which encourages semantically similar regions to be given the same label. Extensive experiments on two publicly fundus datasets show that the proposed approach significantly outperforms other state-of-theart comparison algorithms' (Our code is available at https://***/ fengweie/UDA_CLR.).

关键词： Unsupervised domain adaptation Category level regularization Fundus image segmentation

来源：评论

学校读者我要写书评

暂无评论

Domain Specific Convolution and High Frequency Reconstruction Based Unsupervised Domain Adaptation for medical image Segmentation 25th

Domain Specific Convolution and High Frequency Reconstructio...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Hu, Shishuai Liao, Zehui Xia, Yong Northwestern Polytech Univ Natl Engn Lab Integrated Aerosp Ground Ocean Big Sch Comp Sci & Engn Xian 710072 Peoples R China Northwestern Polytech Univ Ningbo Inst Ningbo 315048 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China

ISBN: (纸本)9783031164491;9783031164484

Although deep learning models have achieved remarkable success in medical image segmentation, the domain shift issue caused mainly by the highly variable quality of medical images is a major hurdle that prevents these models from being deployed for real clinical practices, since no one can predict the performance of a 'well-trained' model on a set of unseen clinical data. Previously, many methods have been proposed based on, for instance, CycleGAN or the Fourier transform to address this issue, which, however, suffer from either an inadequate ability to preserve anatomical structures or unexpectedly introduced artifacts. In this paper, we propose a multi-source-domain unsupervised domain adaptation (UDA) method called Domain specific Convolution and high frequency Reconstruction (DoCR) for medical image segmentation. We design an auxiliary high frequency reconstruction (HFR) task to facilitate UDA, and hence avoid the interference of the artifacts generated by the low-frequency component replacement. We also construct the domain specific convolution (DSC) module to boost the segmentation model's ability to domain-invariant features extraction. We evaluate DoCR on a benchmark fundus image dataset. Our results indicate that the proposed DoCR achieves superior performance over other UDA methods in multi-domain joint optic cup and optic disc segmentation. Code is available at: https://***/ShishuaiHu/DoCR.

关键词： Unsupervised domain adaptation medical image segmentation Domain specific convolution High frequency reconstruction

来源：评论

学校读者我要写书评

暂无评论

Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D medical image Segmentation 25th

Self-learning and One-Shot Learning Based Single-Slice Annot...

引用

25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wu, Yixuan Zheng, Bo Chen, Jintai Chen, Danny Z. Wu, Jian Zhejiang Univ Sch Med Hangzhou Peoples R China Zhejiang Univ Polytech Inst Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN USA Zhejiang Univ Affiliated Hosp 2 Sch Med Sch Publ Hlth Hangzhou Peoples R China Zhejiang Univ Inst Wenzhou Hangzhou Peoples R China

ISBN: (纸本)9783031164521;9783031164514

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images. To significantly reduce annotation efforts while attaining competitive segmentation accuracy, we propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image. Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation and propagating the annotated data with the well-trained reconstruction network. Extensive experiments verify that our new framework achieves comparable performance with less than 1% annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets.

关键词： 3D medical image segmentation Sparse annotation Self-learning One-shot learning

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 << < 51 52 53 54 55 56 57 58 59 60 > >>

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：