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
122 篇 期刊文献
85 册 图书

馆藏范围

2,690 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,271 篇 工学
- 2,063 篇 计算机科学与技术...
- 1,060 篇 生物医学工程（可授...
- 847 篇 软件工程
- 508 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
- 144 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
- 17 篇 建筑学
1,767 篇 医学
- 1,333 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

303 篇 image segmentati...
118 篇 image registrati...
105 篇 medical imaging
96 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...
34 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 univ n carolina ...
12 篇 centre for medic...
12 篇 college of physi...
12 篇 shanghai united ...
12 篇 chinese univ hon...
12 篇 univ penn dept r...
11 篇 department of co...
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
24 篇 davatzikos chris...
23 篇 ni dong
23 篇 glocker ben
22 篇 wang qian
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
18 篇 hawkes david j.
17 篇 duncan james s.

语言

2,667 篇 英文
19 篇 其他
8 篇 中文

检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2691 条记录，以下是381-390 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：

Democratizing Pathological image Segmentation with Lay Annotators via Molecular-Empowered Learning 26th

Democratizing Pathological Image Segmentation with Lay Annot...

引用

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

作者： Deng, Ruining Li, Yanwei Li, Peize Wang, Jiacheng Remedios, Lucas W. Agzamkhodjaev, Saydolimkhon Asad, Zuhayr Liu, Quan Cui, Can Wang, Yaohong Wang, Yihan Tang, Yucheng Yang, Haichun Huo, Yuankai Vanderbilt Univ Nashville TN 37215 USA NVIDIA Corp Santa Clara CA USA NVIDIA Corp Bethesda MD USA Vanderbilt Univ Med Ctr Nashville TN 37232 USA

ISBN: (纸本)9783031439865;9783031439872

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). the contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators;(2) the proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists;(3) A deep corrective learning (learning with imperfect labels) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. the official implementation and cell annotations are publicly available at https://***/hrlblab/MolecularEL.

关键词： image annotation Registration Noisy label learning Pathology

来源：评论

学校读者我要写书评

暂无评论

Federated Condition Generalization on Low-dose CT Reconstruction via Cross-domain Learning 1

引用

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

作者： Chen, Shixuan Cao, Boxuan Du, Yinda Zhang, Yaoduo He, Ji Bian, Zhaoying Zeng, Dong Ma, Jianhua Southern Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China Southern Med Univ Zhujiang Hosp Dept Radiol Guangzhou Guangdong Peoples R China Pazhou Lab Huangpu Guangzhou Guangdong Peoples R China Guangzhou Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

the harmful radiation dose associated with CT imaging is a major concern because it can cause genetic diseases. Acquiring CT data at low radiation doses has become a pressing goal. Deep learning (DL)-based methods have proven to suppress noise-induced artifacts and promote image quality in low-dose CT imaging. However, it should be noted that most of the DL-based methods are constructed based on the CT data from a specific condition, i.e., specific imaging geometry and specific dose level. then these methods might generalize poorly to the other conditions, i.e., different imaging geometries and other radiation doses, due to the big data heterogeneity. In this study, to address this issue, we propose a condition generalization method under a federated learning framework (FedCG) to reconstruct CT images on two conditions: three different dose levels and different sampling shcemes at three different geometries. Specifically, the proposed FedCG method leverages a cross-domain learning approach: individual-client sinogram learning and cross-client image reconstruction for condition generalization. In each individual client, the sinogram at each condition is processed similarly to that in the iRadonMAP. then the CT images at each client are learned via a condition generalization network in the server which considers latent common characteristics in the CT images at all conditions and preserves the client-specific characteristics in each condition. Experiments show that the proposed FedCG outperforms the other competing methods on two imaging conditions in terms of qualitative and quantitative assessments.

关键词： low-dose CT image reconstruction federal learning generalization generalization

来源：评论

学校读者我要写书评

暂无评论

3D medical image Segmentation with Sparse Annotation via Cross-Teaching Between 3D and 2D Networks 1

引用

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

作者： Cai, Heng Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang Nanjing Univ Natl Inst Hlth Care Data Sci State Key Lab Novel Software Technol Nanjing Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Shandong Womens Univ Sch Data & Comp Sci Jinan Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

medical image segmentation typically necessitates a large and precisely annotated dataset. However, obtaining pixel-wise annotation is a labor-intensive task that requires significant effort from domain experts, making it challenging to obtain in practical clinical scenarios. In such situations, reducing the amount of annotation required is a more practical approach. One feasible direction is sparse annotation, which involves annotating only a few slices, and has several advantages over traditional weak annotation methods such as bounding boxes and scribbles, as it preserves exact boundaries. However, learning from sparse annotation is challenging due to the scarcity of supervision signals. To address this issue, we propose a framework that can robustly learn from sparse annotation using the cross-teaching of both 3D and 2D networks. Considering the characteristic of these networks, we develop two pseudo label selection strategies, which are hard-soft confidence threshold and consistent label fusion. Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound result. Our code is available at https://***/HengCai-NJU/3D2DCT.

关键词： 3D segmentation Sparse annotation Cross-teaching

来源：评论

学校读者我要写书评

暂无评论

Adaptive Region Selection for Active Learning in Whole Slide image Semantic Segmentation 26th

Adaptive Region Selection for Active Learning in Whole Slide...

引用

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

作者： Qiu, Jingna Wilm, Frauke Oettl, Mathias Schlereth, Maja Liu, Chang Heimann, Tobias Aubreville, Marc Breininger, Katharina Friedrich Alexander Univ Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Pattern Recognit Lab Erlangen Germany Siemens Healthineers Digital Technol & Innovat Erlangen Germany Tech Hsch Ingolstadt Ingolstadt Germany

ISBN: (纸本)9783031438943;9783031438950

the process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. Region-based active learning (AL) involves training the model on a limited number of annotated image regions instead of requesting annotations of the entire images. these annotation regions are iteratively selected, with the goal of optimizing model performance while minimizing the annotated area. the standard method for region selection evaluates the informativeness of all square regions of a specified size and then selects a specific quantity of the most informative regions. We find that the efficiency of this method highly depends on the choice of AL step size (i.e., the combination of region size and the number of selected regions per WSI), and a suboptimal AL step size can result in redundant annotation requests or inflated computation costs. this paper introduces a novel technique for selecting annotation regions adaptively, mitigating the reliance on this AL hyperparameter. Specifically, we dynamically determine each region by first identifying an informative area and then detecting its optimal bounding box, as opposed to selecting regions of a uniform predefined shape and size as in the standard method. We evaluate our method using the task of breast cancer metastases segmentation on the public CAMELYON16 dataset and show that it consistently achieves higher sampling efficiency than the standard method across various AL step sizes. With only 2.6% of tissue area annotated, we achieve full annotation performance and thereby substantially reduce the costs of annotating a WSI dataset. the source code is available at https://***/ DeepMicroscopy/AdaptiveRegionSelection.

关键词： Active learning Region selection Whole slide images

来源：评论

学校读者我要写书评

暂无评论

Retinal Age Estimation with Temporal Fundus images Enhanced Progressive Label Distribution Learning 26th

Retinal Age Estimation with Temporal Fundus Images Enhanced ...

引用

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

作者： Yu, Zhen Chen, Ruiye Gui, Peng Ju, Lie Shang, Xianwen Zhu, Zhuoting He, Mingguang Ge, Zongyuan Monash Univ Cent Clin Sch Fac Med Nursing & Hlth Sci Melbourne Vic Australia Monash Univ AIM Hlth Lab Melbourne Vic Australia Monash Univ Monash Med AI Melbourne Vic Australia Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Univ Melbourne Ctr Eye Res Australia Melbourne Vic Australia Univ Melbourne Dept Surg Ophthalmol Melbourne Vic Australia Monash Univ Fac Engn Melbourne Vic Australia Monash Univ Fac IT Melbourne Vic Australia

ISBN: (纸本)9783031439896;9783031439902

Retinal age has recently emerged as a reliable ageing biomarker for assessing risks of ageing-related diseases. Several studies propose to train deep learning models to estimate retinal age from fundus images. However, the limitation of these studies lies in 1) both of them only train models on snapshot images from single cohorts;2) they ignore label ambiguity and individual variance in the modeling part. In this study, we propose a progressive label distribution learning (LDL) method with temporal fundus images to improve the retinal age estimation on snapshot fundus images from multiple cohorts. First, we design a two-stage LDL regression head to estimate adaptive age distribution for individual images. then, we eliminate cohort variance by introducing ordinal constraints to align image features from different data sources. Finally, we add a temporal branch to model sequential fundus images and use the captured temporal evolution as auxiliary knowledge to enhance the model's predictive performance on snapshot fundus images. We use a large retinal fundus image dataset which consists of similar to 130k images from multiple cohorts to verify our method. Extensive experiments provide evidence that our model can achieve lower age prediction errors than existing methods.

关键词： Retinal age estimation label distribution learning temporal fundus image

来源：评论

学校读者我要写书评

暂无评论

SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain medical image Segmentation 26th

SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain ...

引用

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

作者： Zhang, Yuhan Huang, Kun Chen, Cheng Chen, Qiang Pheng-Ann Heng Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Inst Med Intelligence & XR Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China Nanjing Univ Sci & Technol Dept Comp Sci & Engn Nanjing Peoples R China Harvard Med Sch Ctr Adv Med Comp & Anal Boston MA 02115 USA Massachusetts Gen Hosp Boston MA 02114 USA

ISBN: (纸本)9783031438943;9783031438950

Cross-domain distribution shift is a common problem for medical image analysis because medical images from different devices usually own varied domain distributions. Test-time adaptation (TTA) is a promising solution by efficiently adapting source-domain distributions to target-domain distributions at test time with unsupervised manners, which has increasingly attracted important attention. Previous TTA methods applied to medical image segmentation tasks usually carry out a global domain adaptation for all semantic categories, but global domain adaptation would be sub-optimal as the influence of domain shift on different semantic categories may be different. To obtain improved domain adaptation results for different semantic categories, we propose Semantic-Aware Test-Time Adaptation (SATTA), which can individually update the model parameters to adapt to target-domain distributions for each semantic category. Specifically, SATTA deploys an uncertainty estimation module to measure the discrepancies of semantic categories in domain shift effectively. then, a semantic adaptive learning rate is developed based on the estimated discrepancies to achieve a personalized degree of adaptation for each semantic category. Lastly, semantic proxy contrastive learning is proposed to individually adjust the model parameters with the semantic adaptive learning rate. Our SATTA is extensively validated on retinal fluid segmentation based on SD-OCT images. the experimental results demonstrate that SATTA consistently improves domain adaptation performance on semantic categories over other state-of-the-art TTA methods.

关键词： test-time adaptation domain shift medical image segmentation

来源：评论

学校读者我要写书评

暂无评论

Collaborative Modality Generation and Tissue Segmentation for Early-Developing Macaque Brain MR images 26th

Collaborative Modality Generation and Tissue Segmentation fo...

引用

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

作者： Wu, Xueyang Zhong, Tao Liang, Shujun Wang, Li Li, Gang Zhang, Yu Southern Med Univ Sch Biomed Engn Guangzhou 510515 Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc Guangzhou 510515 Peoples R China Southern Med Univ Guangdong Prov Engn Lab Med Imaging & Diagnost Te Guangzhou 510515 Peoples R China Univ N Carolina Dept Radiol Chapel Hill NC USA Univ N Carolina BRIC Chapel Hill NC 27515 USA

ISBN: (纸本)9783031439001;9783031439018

In neuroscience research, automatic segmentation of macaque brain tissues in magnetic resonance imaging (MRI) is crucial for understanding brain structure and function during development and evolution. Acquisition of multimodal information is a key enabler of accurate tissue segmentation, especially in early-developing macaques with extremely low contrast and dynamic myelination. However, many MRI scans of early-developing macaques are acquired only in a single modality. While various generative adversarial networks (GAN) have been developed to impute missing modality data, current solutions treat modality generation and image segmentation as two independent tasks, neglecting their inherent relationship and mutual benefits. To address these issues, this study proposes a novel Collaborative Segmentation-Generation Framework (CSGF) that enables joint missing modality generation and tissue segmentation of macaque brain MR images. Specifically, the CSGF consists of a modality generation module (MGM) and a tissue segmentation module (TSM) that are trained jointly by a cross-module feature sharing (CFS) and transferring generated modality. the training of the MGM under the supervision of the TSM enforces anatomical feature consistency, while the TSM learns multi-modality information related to anatomical structures from both real and synthetic multi-modality MR images. Experiments show that the CSGF outperforms the conventional independent-task mode on an early-developing macaque MRI dataset with 155 scans, achieving superior quality in both missing modality generation and tissue segmentation.

关键词： Tissue segmentation Missing-modality generation Macaque Early brain development Multi-task collaboration

来源：评论

学校读者我要写书评

暂无评论

Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS)

Tracking Lesion Evolution Using a Boundary Enhanced Approac...

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： Mathur, Prateek Kelly, Brendan S. Killeen, Ronan P Lawlor, Aonghus School of Computer Science University College Dublin Dublin Ireland School of Medicine University College Dublin Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin Ireland St. Vincent’s University Hospital Dublin Ireland

ISBN: (纸本)9783031845246

Multiple sclerosis (MS) is a chronic disease of the Central Nervous System (CNS) primarily characterised on Magnetic Resonance Imaging (MRI) by hyper-intense lesions. Accurate and timely identification of new lesions or growth in existing lesions has a tremendous impact on treatment planning and patient care. Early detection can lead to prompt intervention possibly slowing the disease progression. However, this task is challenging, costly and often prone to observer bias. Our approach consists of three key components (1) redefining the lesion segmentation problem as a change detection challenge focusing only on new and enhancing lesions;(2) adding an additional boundary detection task to an existing Convolutional Neural Network (CNN) to provide detailed context regarding the shape, size, and evolution of enhancing lesions;(3) evaluating the Fβ loss objective as an alternative to traditional segmentation loss objectives, aiming to optimise the model’s sensitivity and specificity. Furthermore, we employ Gradient-weighted Class Activation Mapping (GradCAM) to visualise the network’s attention, aiding in the interpretation and validation of our approach. By providing an objective evaluation, our method aims to streamline clinical decision-making processes for improved patient outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning 26th

atTRACTive: Semi-automatic White Matter Tract Segmentation U...

引用

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

作者： Peretzke, Robin Maier-Hein, Klaus H. Bohn, Jonas Kirchhoff, Yannick Roy, Saikat Oberli-Palma, Sabrina Becker, Daniela Lenga, Pavlina Neher, Peter German Canc Res Ctr Div Med Image Comp MIC Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany German Canc Consortium DKTK Partner Site Heidelberg Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany DKFZ Heidelberg Germany Univ Med Ctr Heidelberg Heidelberg Germany Univ Heidelberg Hosp Pattern Anal & Learning Grp Heidelberg Germany HIDSS4Health Helmholtz Informat & Data Sci Sch Hlth Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany Heidelberg Univ Fac Biosci Heidelberg Germany Univ Heidelberg Hosp Dept Neurosurg Heidelberg Germany IU Int Univ Appl Sci Erfurt Germany

ISBN: (纸本)9783031439926;9783031439933

Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learning models have reached state-of-the-art solving this task automatically. However, these models are primarily trained on healthy subjects and struggle with strong anatomical aberrations, e.g. caused by brain tumors. this limitation makes them unsuitable for tasks such as preoperative planning, wherefore time-consuming and challenging manual delineation of the target tract is typically employed. We propose semi-automatic entropy-based active learning for quick and intuitive segmentation of white matter tracts from whole-brain tractography consisting of millions of streamlines. the method is evaluated on 21 openly available healthy subjects from the Human Connectome Project and an internal dataset of ten neurosurgical cases. With only a few annotations, the proposed approach enables segmenting tracts on tumor cases comparable to healthy subjects (dice = 0.71), while the performance of automatic methods, like TractSeg dropped substantially (dice = 0.34) in comparison to healthy subjects. the method is implemented as a prototype named atTRACTive in the freely available software MITK Diffusion. Manual experiments on tumor data showed higher efficiency due to lower segmentation times compared to traditional ROI-based segmentation.

关键词： DWI and tractography Active learning Tract segmentation Fiber tracking

来源：评论

学校读者我要写书评

暂无评论

MS-MT++: Enhanced Multi-scale Mean Teacher for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation 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： Zhao, Ziyuan Lin, Ruikai Xu, Kaixin Yang, Xulei Guan, Cuntai A*STAR Singapore Singapore

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Domain shift has been a long-standing issue for medical image segmentation. Unsupervised domain adaptation (UDA) methods have recently achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. Different from CrossMoDA 2022, the challenge of 2023 includes highly heterogeneous MRI scans from more institutions and various scanners and subdivides the segmentation object into three key brain structures (intra/extra-vestibular schwannoma and cochlea), increasing the difficulty of domain adaptation. In this work, we improve our previous method and propose an enhanced multi-scale self-ensembling-based UDA framework for automatic segmentation of Vestibular Schwannoma and Cochlea on high-resolution T2 images. Our method demonstrates a mean Dice score of 0.669 and 0.766 for the extra/intra-VS joint area and Cochlea respectively, securing a top 5 finish in the CrossMoDA 2023 challenge. © 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 << < 35 36 37 38 39 40 41 42 43 44 > >>

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：