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431-440 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：

SSL Based Encoder Pretraining for Segmenting a Heterogeneous Chronic Wound image Database with Few Annotations 4th

SSL Based Encoder Pretraining for Segmenting a Heterogeneo...

引用

4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Picaud, Guillaume Chaumont, Marc Subsol, Gérard Téot, Luc LIRMM équipe ICAR Univ. Montpellier CNRS Montpellier France Univ. Nîmes Place Gabriel Péri Nîmes France Cicat-Occitanie Montpellier France

ISBN: (纸本)9783031808708

Segmentation is crucial in medical imaging, but obtaining a sufficient quantity of annotated data is challenging, limiting the development of high-performing deep learning models. Self-supervised learning (SSL) strategies offer a promising solution to address this lack of annotation. One such strategy, Dinov2 for Distillation with NO labels, enabled the creation of the vast LVD-142M database and the training of encoders, whose weights are now freely accessible. However, clinical images may not be well represented in LVD-142M. thus, in the context of scarce annotated clinical data, we evaluate the benefits of a generic encoder pre-trained with DINO on LVD-142M versus a lighter one. We also explore the effect of SSL DINO pre-training strategy directly on the target dataset. We measure the impact of available label quantity on segmentation performances. Results show, in a context with few annotated images, specific and lightweight encoder can outperform generically pre-trained DINO one. Furthermore, DINO SSL pre-training on specific dataset is beneficial for small encoder. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis

Physics-Informed Latent Diffusion for Multimodal Brain MRI ...

引用

Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Lüpke, Sven Yeganeh, Yousef Adeli, Ehsan Navab, Nassir Farshad, Azade Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Stanford University Stanford United States

ISBN: (纸本)9783031845246

Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To address this, we present a novel physics-informed generative model capable of synthesizing a variable number of brain MRI modalities, including those not present in the original dataset. Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan. Our experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility. Furthermore, we validate the distributions of generated tissue properties by comparing them to those measured in real brain tissue. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Beyond MobileNet: An Improved MobileNet for Retinal Diseases 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： Zhu, Wenhui Qiu, Peijie Chen, Xiwen Li, Huayu Wang, Hao Lepore, Natasha Dumitrascu, Oana M. Wang, Yalin School of Computing and Augmented Intelligence Arizona State University TempeAZ United States McKeley School of Engineering Washington University in St. Louis St. LouisMO United States School of Computing Clemson University ClemsonSC United States Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States Department of Neurology Mayo Clinic PhoenixAZ United States CIBORG Lab Department of Radiology Children’s Hospital Los Angeles Los AngelesCA United States

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy (MM) is the leading cause of severe vision loss or blindness. Deep learning-based automated tools are indispensable in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated model designs, however, are prone to be overfitting and hinder their advantages in specific tasks in medical image analysis. In this work, we argue that a well-calibrated CNN model may mitigate these problems. To this end, we empirically investigated the macro and micro designs of a CNN and its training strategies by starting with a standard MobileNet. Based on the investigation, we proposed a lightweight MobileNet training framework equipped with a series of optimal parameters and modules based on retinal images. As a result of performance, our model secured third place in the MICCAI MMAC 2023 Challenge - Classification of Myopic Maculopathy. Our software package is available at https://***/Retinal-Research/NN-MOBILENET. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

CRISP - Reliable Uncertainty Estimation for medical image Segmentation 25th

CRISP - Reliable Uncertainty Estimation for Medical Image Se...

引用

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

作者： Judge, thierry Bernard, Olivier Porumb, Mihaela Chartsias, Agisilaos Beqiri, Arian Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada Ultromics Ltd Oxford OX4 2SU England Univ Lyon Univ Lyon 1 INSA Lyon CREATISCNRS UMR5220Inserm U1294 Villeurbanne France

ISBN: (纸本)9783031164521;9783031164514

Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. the independent pixel-wise uncertainty estimates, often based on the probabilistic interpretation of neural networks, do not take into account anatomical prior knowledge and consequently provide sub-optimal results to many segmentation tasks. For this reason, we propose CRISP a ContRastive image Segmentation for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations and their corresponding images. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps. Comprehensive studies performed on four medical image databases involving different modalities and organs underlines the superiority of our method compared to state-of-the-art approaches. Code is available at: https://***/thierryJudge/CRISP-uncertainty.

关键词： medical imaging Segmentation Uncertainty Deep learning

来源：评论

学校读者我要写书评

暂无评论

ProCo: Prototype-Aware Contrastive Learning for Long-Tailed medical image Classification 25th

ProCo: Prototype-Aware Contrastive Learning for Long-Tailed ...

引用

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

作者： Yang, Zhixiong Pan, Junwen Yang, Yanzhan Shi, Xiaozhou Zhou, Hong-Yu Zhang, Zhicheng Bian, Cheng Xiaohe Healthcare ByteDance Guangzhou Peoples R China Univ Hong Kong Dept Comp Sci Pokfulam Hong Kong Peoples R China

ISBN: (纸本)9783031164521;9783031164514

medical image classification has been widely adopted in medical image analysis. However, due to the difficulty of collecting and labeling data in the medical area, medical image datasets are usually highly-imbalanced. To address this problem, previous works utilized class samples as prior for re-weighting or re-sampling but the feature representation is usually still not discriminative enough. In this paper, we adopt the contrastive learning to tackle the long-tailed medical imbalance problem. Specifically, we first propose the category prototype and adversarial proto-instance to generate representative contrastive pairs. then, the prototype recalibration strategy is proposed to address the highly imbalanced data distribution. Finally, a unified proto-loss is designed to train our framework. the overall framework, namely as Prototype-aware Contrastive learning (ProCo), is unified as a single-stage pipeline in an end-to-end manner to alleviate the imbalanced problem in medical image classification, which is also a distinct progress than existing works as they follow the traditional two-stage pipeline. Extensive experiments on two highly-imbalanced medical image classification datasets demonstrate that our method outperforms the existing state-of-the-art methods by a large margin. Our source codes are available at https://***/skyz215/ProCo.

关键词： Contrastive learning Prototype Imbalanced dataset

来源：评论

学校读者我要写书评

暂无评论

MSKdeX: Musculoskeletal (MSK) Decomposition from an X-Ray image for Fine-Grained Estimation of Lean Muscle Mass and Muscle Volume 26th

MSKdeX: Musculoskeletal (MSK) Decomposition from an X-Ray Im...

引用

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

作者： Gu, Yi Otake, Yoshito Uemura, Keisuke Takao, Masaki Soufi, Mazen Hiasa, Yuta Talbot, Hugues Okada, Seiji Sugano, Nobuhiko Sato, Yoshinobu Nara Inst Sci & Technol Grad Sch Sci & Technol Div Informat Sci Ikoma Japan Univ Paris Saclay CentraleSupelec Paris France Osaka Univ Dept Orthopaed Grad Sch Med Suita Osaka Japan Ehime Univ Dept Bone & Joint Surg Grad Sch Med Toon Japan Osaka Univ Dept Orthopaed Med Engn Grad Sch Med Suita Osaka Japan

ISBN: (纸本)9783031439896;9783031439902

Musculoskeletal diseases such as sarcopenia and osteoporosis are major obstacles to health during aging. Although dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) can be used to evaluate musculoskeletal conditions, frequent monitoring is difficult due to the cost and accessibility (as well as high radiation exposure in the case of CT). We propose a method (named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray image, a low-cost, low-radiation, and highly accessible imaging modality, through musculoskeletal decomposition leveraging fine-grained segmentation in CT. We train a multi-channel quantitative image translation model to decompose an X-ray image into projections of CT of individual muscles to infer the lean muscle mass and muscle volume. We propose the object-wise intensity-sum loss, a simple yet surprisingly effective metric invariant to muscle deformation and projection direction, utilizing information in CT and X-ray images collected from the same patient. While our method is basically an unpaired image-to-image translation, we also exploit the nature of the bone's rigidity, which provides the paired data through 2D-3D rigid registration, adding strong pixel-wise supervision in unpaired training. through the evaluation using a 539-patient dataset, we showed that the proposed method significantly outperformed conventional methods. the average Pearson correlation coefficient between the predicted and CT-derived ground truth metrics was increased from 0.460 to 0.863. We believe our method opened up a new musculoskeletal diagnosis method and has the potential to be extended to broader applications in multi-channel quantitative image translation tasks.

关键词： Muscles Radiography Generative adversarial networks (GAN) Sarcopenia image-to-image translation

来源：评论

学校读者我要写书评

暂无评论

An Auto-Encoder to Reconstruct Structure with Cryo-EM images via theoretically Guaranteed Isometric Latent Space, and Its Application for Automatically computing the Conformational Pathway 26th

An Auto-Encoder to Reconstruct Structure with Cryo-EM Images...

引用

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

作者： Yamazaki, Kimihiro Wada, Yuichiro Tokuhisa, Atsushi Wada, Mutsuyo Katoh, Takashi Umeda, Yuhei Okuno, Yasushi Nakagawa, Akira Fujitsu Ltd Kawasaki Kanagawa Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan RIKEN Ctr Comp Sci Kobe Hyogo Japan Kyoto Univ Grad Sch Med Kyoto Japan

ISBN: (纸本)9783031439063;9783031439070

Structural analysis by cryo-electron microscopy (Cryo-EM) has become well-established in the field of structural biology. Recently, cutting-edge methods have been proposed for the purpose of reconstructing either a small set of structures or a conformational pathway (continuous structural change), where a 3D density map represents the structure. However, we usually perform heavy manual labor to define the plausible pathway related to biological significance. In this study, for automatizing such manual labor, we propose a deep Auto-Encoder (AE) with a trainable prior. the AE is trained using only a set of single particle Cryo-EM images. the trained AE reconstructs the corresponding structures for the latent variables of the Cryo-EM images. the latent distribution can not only be theoretically proportional to a distribution of the structure but also consistent with the trained prior. Taking advantage of this property, we can automatically compute the pathway by only accessing the latent space as follows: i) generating a ridgeline on the latent distribution and ii) defining the conformational pathway as a sequence of the reconstructed structures along the ridgeline using the trained decoder. In our numerical experiments, we evaluate the computed pathways by comparing them with existing ones that were manually determined by other researchers, and confirm that they are sufficiently consistent.

关键词： Cryo-EM Conformational Pathway Deep Learning 3D Reconstruction Structural Biology

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation for Improving CNNs in medical image Classification 8

Data Augmentation for Improving CNNs in Medical Image Classi...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Ren, Yi He, Zengmin Deng, Yang Huang, Bo Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China

ISBN: (纸本)9798350302455

medical image is essential for physicians to diagnose diseases. And convolutional neural networks (CNNs) have gained momentum for computer-aided diagnosis (CAD) medical image. However, there are still challenges in CNNs related to a lack of data and class-imbalanced datasets. Data augmentation is utilized to address the issues mentioned above. In this study, the Pseudo-color enhancement algorithm (OCEAN and TWILIGHT), common linear transform, CLAHE enhancement, and K-means clustering algorithm were applied to pneumonia X-ray images for data augmentation. thus, five processed datasets were finally obtained. the results indicated that five data-augmentation methods effectively improved the performance of CNNs for the pneumonia X-ray images binary classification detection, in which the CLAHE enhancement and OCEAN enhancement methods had the accuracy and F1 Score with 97.42%, 97.19%, and 97.42%, 98.36%, respectively. DenseNet121 showed the best classification performance of four CNNs utilized in this study, as evidenced by the area under the receiver operating characteristic (ROC) curves (AUC). To verify the generalizability of the two enhancement methods, CLAHE and OCEAN enhancement methods were applied to the Magnetic Resonance Imaging (MRI) dataset of Alzheimer's disease, which had the accuracy and F1 Score with 98.15% , 98.15%, and 97.22%, 97.68%, respectively. therefore, these simple and efficient data-augmentation methods effectively improve the performance of CNNs for diagnosing medical image, which can provide theoretical reference for physicians to diagnose the above two diseases, and advance the application of clinical medicine. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Flat-Aware Cross-Stage Distilled Framework for Imbalanced medical image Classification 1

引用

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

作者： Li, Jinpeng Chen, Guangyong Mao, Hangyu Deng, Danruo Li, Dong Hao, Jianye Dou, Qi Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Zhejiang Lab Hangzhou Zhejiang Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

medical data often follow imbalanced distributions, which poses a long-standing challenge for computer-aided diagnosis systems built upon medical image classification. Most existing efforts are conducted by applying re-balancing methods for the collected training samples, which improves the predictive performance for the minority class but at the cost of decreasing the performance for the majority. To address this paradox, we adopt a flat-aware cross-stage distilled framework (FCD), where we first search for flat local minima of the base training objective function on the original imbalanced dataset, and then continuously finetune this classifier within the flat region on the re-balanced one. To further prevent the performance decreasing for the majority, we propose a cross-stage distillation regularizing term to promote the optimized features to remain in the common optimal subspace. Extensive experiments on two imbalanced medical image datasets demonstrate the effectiveness of our proposed framework and its generality in improving the performance of existing imbalanced methods. the code of this work will be released publicly.

关键词： Imbalanced image classification Flat-aware optimization Cross-stage distillation

来源：评论

学校读者我要写书评

暂无评论

Local Synthesis of Healthy Brain Tissue Using an Enhanced 3D Pix2Pix Model for medical image Inpainting 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： Sadique, M.S. Rahman, M.M. Farzana, W. Glandon, A. Temtam, A. Iftekharuddin, K.M. Old Dominion University NorfolkVA23529 United States

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

the restoration of voided regions in three-dimensional (3D) brain Magnetic Resonance Imaging (MRI) data is a critical task in medical image processing. this paper proposes a 3D pix2pix GAN modeling strategy for 3D brain MRI image inpainting, with the goal of improving the quality and diagnostic utility of medical images. the proposed method renders advanced algorithms to accurately predict and reconstruct the voided regions of 3D brain MRI scans. the inpainting outcomes are assessed via online evaluation on BraTS-2023 inpainting challenge data using established metrics, such as the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). the experimental results demonstrate the effectiveness of the method in achieving a mean SSIM, PSNR, and MSE score of 0.70, 17.52, and 0.049, respectively, indicating spatial coherence and visual accuracy. the improved PSNR and reduced MSE further underscore the quality enhancement achieved by the proposed inpainting method. this work contributes to the field of medical imaging by addressing the crucial challenge of 3D brain MRI image inpainting and demonstrates its potential to enhance medical image analysis, diagnosis, and patient care. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共270页 << < 40 41 42 43 44 45 46 47 48 49 > >>

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：