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Proportion Estimation by Masked Learning from Label Proportion 通过标签比例的掩码学习进行比例估计
DALI@MICCAI Pub Date : 2024-05-08 DOI: 10.1007/978-3-031-58171-7_12
Takumi Okuo, Kazuya Nishimura, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise
{"title":"Proportion Estimation by Masked Learning from Label Proportion","authors":"Takumi Okuo, Kazuya Nishimura, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise","doi":"10.1007/978-3-031-58171-7_12","DOIUrl":"https://doi.org/10.1007/978-3-031-58171-7_12","url":null,"abstract":"","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":" 10","pages":"117-126"},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Noisy Label Classification Using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning 基于测试时间增强交叉熵和NoiseMix学习的标签噪声选择的噪声标签分类
DALI@MICCAI Pub Date : 2022-12-01 DOI: 10.1007/978-3-031-17027-0_8
Han S. Lee, Haeil Lee, H. Hong, Junmo Kim
{"title":"Noisy Label Classification Using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning","authors":"Han S. Lee, Haeil Lee, H. Hong, Junmo Kim","doi":"10.1007/978-3-031-17027-0_8","DOIUrl":"https://doi.org/10.1007/978-3-031-17027-0_8","url":null,"abstract":"","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126416865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation CTooth+:大型牙体锥形束计算机断层数据集和牙体分割基准
DALI@MICCAI Pub Date : 2022-08-02 DOI: 10.48550/arXiv.2208.01643
Weiwei Cui, Yaqi Wang, Yilong Li, Dansheg Song, Xingyong Zuo, Jiaojiao Wang, Yifan Zhang, Huiyu Zhou, B. Chong, L. Zeng, Qianni Zhang
{"title":"CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation","authors":"Weiwei Cui, Yaqi Wang, Yilong Li, Dansheg Song, Xingyong Zuo, Jiaojiao Wang, Yifan Zhang, Huiyu Zhou, B. Chong, L. Zeng, Qianni Zhang","doi":"10.48550/arXiv.2208.01643","DOIUrl":"https://doi.org/10.48550/arXiv.2208.01643","url":null,"abstract":". Accurate tooth volume segmentation is a prerequisite for computer-aided dental analysis. Deep learning-based tooth segmentation methods have achieved satisfying performances but require a large quantity of tooth data with ground truth. The dental data publicly available is limited meaning the existing methods can not be reproduced, evaluated and applied in clinical practice. In this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully annotated volumes and 146 unlabeled volumes. We further evaluate several state-of-the-art tooth volume segmentation strategies based on fully-supervised learning, semi-supervised learning and active learning, and define the performance principles. This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development. The codebase and dataset are released here.","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131881329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Disentangling A Single MR Modality 解开单个MR模态
DALI@MICCAI Pub Date : 2022-05-10 DOI: 10.48550/arXiv.2205.04982
Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, M. Bilgel, S. Resnick, Jerry L Prince, A. Carass
{"title":"Disentangling A Single MR Modality","authors":"Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, M. Bilgel, S. Resnick, Jerry L Prince, A. Carass","doi":"10.48550/arXiv.2205.04982","DOIUrl":"https://doi.org/10.48550/arXiv.2205.04982","url":null,"abstract":"Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks. Current methods learn disentangled representations using either paired multi-modal images with the same underlying anatomy or auxiliary labels (e.g., manual delineations) to provide inductive bias for disentanglement. However, these requirements could significantly increase the time and cost in data collection and limit the applicability of these methods when such data are not available. Moreover, these methods generally do not guarantee disentanglement. In this paper, we present a novel framework that learns theoretically and practically superior disentanglement from single modality magnetic resonance images. Moreover, we propose a new information-based metric to quantitatively evaluate disentanglement. Comparisons over existing disentangling methods demonstrate that the proposed method achieves superior performance in both disentanglement and cross-domain image-to-image translation tasks.","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130538998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data 基于多模态合成数据的分层级联脑肿瘤分割方法
DALI@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-17027-0_10
Yasmina Alkhalil, Aymen Ayaz, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer
{"title":"A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data","authors":"Yasmina Alkhalil, Aymen Ayaz, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer","doi":"10.1007/978-3-031-17027-0_10","DOIUrl":"https://doi.org/10.1007/978-3-031-17027-0_10","url":null,"abstract":"","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129589548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Image Synthesis-Based Late Stage Cancer Augmentation and Semi-supervised Segmentation for MRI Rectal Cancer Staging 基于图像合成的晚期肿瘤增强和半监督分割的MRI直肠癌分期
DALI@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-17027-0_1
Saeko Sasuga, Akira Kudo, Y. Kitamura, S. Iizuka, E. Simo-Serra, A. Hamabe, Masayuki Ishii, I. Takemasa
{"title":"Image Synthesis-Based Late Stage Cancer Augmentation and Semi-supervised Segmentation for MRI Rectal Cancer Staging","authors":"Saeko Sasuga, Akira Kudo, Y. Kitamura, S. Iizuka, E. Simo-Serra, A. Hamabe, Masayuki Ishii, I. Takemasa","doi":"10.1007/978-3-031-17027-0_1","DOIUrl":"https://doi.org/10.1007/978-3-031-17027-0_1","url":null,"abstract":"","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants CSGAN: 6个月婴儿的合成辅助脑MRI分割
DALI@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-17027-0_9
Xin Tang, Jiadong Zhang, Yongsheng Pan, Yuyao Zhang, F. Shi
{"title":"CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants","authors":"Xin Tang, Jiadong Zhang, Yongsheng Pan, Yuyao Zhang, F. Shi","doi":"10.1007/978-3-031-17027-0_9","DOIUrl":"https://doi.org/10.1007/978-3-031-17027-0_9","url":null,"abstract":"","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133825996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Lesser of Two Evils Improves Learning in the Context of Cortical Thickness Estimation Models - Choose Wisely 两害相权取其轻在皮质厚度估计模型的背景下提高学习-明智地选择
DALI@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-17027-0_4
Filip Rusak, Rodrigo Santa Cruz, Elliot Smith, J. Fripp, C. Fookes, P. Bourgeat, Andrew P. Bradley
{"title":"Lesser of Two Evils Improves Learning in the Context of Cortical Thickness Estimation Models - Choose Wisely","authors":"Filip Rusak, Rodrigo Santa Cruz, Elliot Smith, J. Fripp, C. Fookes, P. Bourgeat, Andrew P. Bradley","doi":"10.1007/978-3-031-17027-0_4","DOIUrl":"https://doi.org/10.1007/978-3-031-17027-0_4","url":null,"abstract":"","PeriodicalId":116176,"journal":{"name":"DALI@MICCAI","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123080601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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