Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration

Abstract Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its continuity is lost when the physiological sta...

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Autores principales:	Zengyi Qin, Jiansheng Chen, Zhenyu Jiang, Xumin Yu, Chunhua Hu, Yu Ma, Suhua Miao, Rongsong Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e942291820ca48d59ab2991c456caf30
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spelling	oai:doaj.org-article:e942291820ca48d59ab2991c456caf302021-12-02T13:34:00ZLearning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration10.1038/s41598-020-79007-52045-2322https://doaj.org/article/e942291820ca48d59ab2991c456caf302020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79007-5https://doaj.org/toc/2045-2322Abstract Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its continuity is lost when the physiological state is quantized into a few discrete classes during recording and labeling. The discreteness introduces misalignment between the true value and its label, meaning that these labels are unfortunately imprecise and coarse-grained. Most previous work did not consider the inaccuracy and directly utilized the coarse labels to train the machine learning algorithms, whose predictions are also coarse-grained. In this work, we propose to learn a precise, fine-grained estimation of physiological states using these coarse-grained ground truths. Established on mathematical rigorous proof, we utilize imprecise labels to restore the probabilistic distribution of precise labels in an approximate order-preserving fashion, then the deep neural network learns from this distribution and offers fine-grained estimation. We demonstrate the effectiveness of our approach in assessing the pathological tremor in Parkinson’s Disease and estimating the systolic blood pressure from bioelectrical signals.Zengyi QinJiansheng ChenZhenyu JiangXumin YuChunhua HuYu MaSuhua MiaoRongsong ZhouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zengyi Qin Jiansheng Chen Zhenyu Jiang Xumin Yu Chunhua Hu Yu Ma Suhua Miao Rongsong Zhou Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
description	Abstract Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its continuity is lost when the physiological state is quantized into a few discrete classes during recording and labeling. The discreteness introduces misalignment between the true value and its label, meaning that these labels are unfortunately imprecise and coarse-grained. Most previous work did not consider the inaccuracy and directly utilized the coarse labels to train the machine learning algorithms, whose predictions are also coarse-grained. In this work, we propose to learn a precise, fine-grained estimation of physiological states using these coarse-grained ground truths. Established on mathematical rigorous proof, we utilize imprecise labels to restore the probabilistic distribution of precise labels in an approximate order-preserving fashion, then the deep neural network learns from this distribution and offers fine-grained estimation. We demonstrate the effectiveness of our approach in assessing the pathological tremor in Parkinson’s Disease and estimating the systolic blood pressure from bioelectrical signals.
format	article
author	Zengyi Qin Jiansheng Chen Zhenyu Jiang Xumin Yu Chunhua Hu Yu Ma Suhua Miao Rongsong Zhou
author_facet	Zengyi Qin Jiansheng Chen Zhenyu Jiang Xumin Yu Chunhua Hu Yu Ma Suhua Miao Rongsong Zhou
author_sort	Zengyi Qin
title	Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
title_short	Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
title_full	Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
title_fullStr	Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
title_full_unstemmed	Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
title_sort	learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e942291820ca48d59ab2991c456caf30
work_keys_str_mv	AT zengyiqin learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT jianshengchen learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT zhenyujiang learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT xuminyu learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT chunhuahu learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT yuma learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT suhuamiao learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration AT rongsongzhou learningfinegrainedestimationofphysiologicalstatesfromcoarsegrainedlabelsbydistributionrestoration
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Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration

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