Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20...最新文献

Urban ecology 城市生态学

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-05-26 DOI: 10.4324/9781003085836-6

D. Bartmanski, I. Woodward

引用次数: 0

Epilogue 后记

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-05-26 DOI: 10.4324/9781003085836-7

Dominik Bartmański, I. Woodward

引用次数: 0

Symbolic economy 象征性的经济

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-05-26 DOI: 10.4135/9781446221280.n236

D. Bartmanski, I. Woodward

引用次数: 0

Material economy 物质经济

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-05-26 DOI: 10.5040/9781474280495.ch-002

D. Bartmanski, I. Woodward

引用次数: 0

Being independent 独立

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-05-26 DOI: 10.5040/9781474280495.ch-001

D. Bartmanski, I. Woodward

引用次数: 0

Epilogue 后记

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-01-01 DOI: 10.5040/9781474280495.0010

引用次数: 0

Prologue 序言

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2020-01-01 DOI: 10.5040/9781474280495.0008

引用次数: 0

Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. 利用以大地距离为导向的密集注意三维密集网进行新生儿大脑分割的多任务学习

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2019-10-01 Epub Date: 2019-10-13 DOI: 10.1007/978-3-030-33391-1_28

Toan Duc Bui, Li Wang, Jian Chen, Weili Lin, Gang Li, Dinggang Shen

{"title":"Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance.","authors":"Toan Duc Bui, Li Wang, Jian Chen, Weili Lin, Gang Li, Dinggang Shen","doi":"10.1007/978-3-030-33391-1_28","DOIUrl":"10.1007/978-3-030-33391-1_28","url":null,"abstract":"The deep convolutional neural network has achieved outstanding performance on neonatal brain MRI tissue segmentation. However, it may fail to produce reasonable results on unseen datasets that have different imaging appearance distributions with the training data. The main reason is that deep learning models tend to have a good fitting to the training dataset, but do not lead to a good generalization on the unseen datasets. To address this problem, we propose a multi-task learning method, which simultaneously learns both tissue segmentation and geodesic distance regression to regularize a shared encoder network. Furthermore, a dense attention gate is explored to force the network to learn rich contextual information. By using three neonatal brain datasets with different imaging protocols from different scanners, our experimental results demonstrate superior performance of our proposed method over the existing deep learning-based methods on the unseen datasets.","PeriodicalId":92891,"journal":{"name":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20...","volume":"11795 ","pages":"243-251"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034948/pdf/nihms-1060328.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37670925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning. 通过成对一致性和对抗性学习实现脑磁共振成像的多域适应性

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20... Pub Date : 2019-01-01 Epub Date: 2019-10-13 DOI: 10.1007/978-3-030-33391-1_7

Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H Sudre, Zach Eaton-Rosen, Lewis J Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M Jorge Cardoso

{"title":"Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning.","authors":"Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H Sudre, Zach Eaton-Rosen, Lewis J Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M Jorge Cardoso","doi":"10.1007/978-3-030-33391-1_7","DOIUrl":"10.1007/978-3-030-33391-1_7","url":null,"abstract":"Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines.","PeriodicalId":92891,"journal":{"name":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 20...","volume":"2019 ","pages":"54-62"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610933/pdf/EMS126674.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39078943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0