Uncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI...最新文献

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings 机器学习在医学成像中安全使用的不确定性:第四届国际研讨会,不确定2022,与MICCAI 2022一起举行,新加坡,2022年9月18日,会议记录
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引用次数: 1
Correction to: Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders 更正:基于图匹配的脑障碍学习连接组生物标志物
Rui Sherry Shen, Jacob A. Alappatt, D. Parker, Junghoon J. Kim, R. Verma, Yusuf Osmanlıoğlu
{"title":"Correction to: Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders","authors":"Rui Sherry Shen, Jacob A. Alappatt, D. Parker, Junghoon J. Kim, R. Verma, Yusuf Osmanlıoğlu","doi":"10.1007/978-3-030-60365-6_21","DOIUrl":"https://doi.org/10.1007/978-3-030-60365-6_21","url":null,"abstract":"","PeriodicalId":93392,"journal":{"name":"Uncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI...","volume":"28 1","pages":"C1 - C1"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82325023","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
Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders. 基于图匹配的连接组生物标志物与脑疾病学习
Rui Sherry Shen, Jacob A Alappatt, Drew Parker, Junghoon Kim, Ragini Verma, Yusuf Osmanlıoğlu
{"title":"Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders.","authors":"Rui Sherry Shen, Jacob A Alappatt, Drew Parker, Junghoon Kim, Ragini Verma, Yusuf Osmanlıoğlu","doi":"10.1007/978-3-030-60365-6_13","DOIUrl":"10.1007/978-3-030-60365-6_13","url":null,"abstract":"<p><p>Advances in neuroimaging techniques such as diffusion MRI and functional MRI enabled evaluation of the brain as an information processing network that is called connectome. Connectomic analysis has led to numerous findings on the organization of the brain its pathological changes with diseases, providing imaging-based biomarkers that help in diagnosis and prognosis. A large majority of connectomic biomarkers benefit either from graph-theoretical measures that evaluate brain's network structure, or use standard metrics such as Euclidean distance or Pearson's correlation to show between-connectomes relations. However, such methods are limited in diagnostic evaluation of diseases, because they do not simultaneously measure the difference between individual connectomes, incorporate disease-specific patterns, and utilize network structure information. To address these limitations, we propose a graph matching based method to quantify connectomic similarity, which can be trained for diseases at functional systems level to provide a subject-specific biomarker assessing the disease. We validate our measure on a dataset of patients with traumatic brain injury and demonstrate that our measure achieves better separation between patients and controls compared to commonly used connectomic similarity measures. We further evaluate the vulnerability of the functional systems to the disease by utilizing the parameter tuning aspect of our method. We finally show that our similarity score correlates with clinical scores, highlighting its potential as a subject-specific biomarker for the disease.</p>","PeriodicalId":93392,"journal":{"name":"Uncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI...","volume":"12443 ","pages":"131-141"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329857/pdf/nihms-1652170.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39285858","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
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis: Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 机器学习在医学成像中的安全使用的不确定性,以及生物医学图像分析中的图形:第二届国际研讨会,不确定2020,第三届国际研讨会,GRAIL 2020,与MICCAI 2020,秘鲁利马,2020年10月8日,会议记录
M. Graham, C. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, P. Nachev, S. Ourselin, M. Cardoso
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引用次数: 7
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