Multi-scored sleep databases: how to exploit the multiple-labels in automated sleep scoring.
Multi-scored sleep databases: how to exploit the multiple-labels in automated sleep scoring.
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多导睡眠图评分中的评分者间差异是一个众所周知的问题。大多数现有的自动睡眠评分系统都是使用由单个评分器标注的标签进行训练的，其主观评价被转移到模型中。当来自两个或更多评分者的注释可用时，评分模型通常在评分者共识上进行训练。平均得分者的主观性被转移到模型中，丢失了关于不同得分者之间的内部变化的信息。在这项研究中，我们的目的是插入不同的医生的多知识到培训过程中。我们的目标是优化模型训练，利用可以从一组评分者的共识中提取的全部信息。我们在三个不同的多评分数据库上训练了两个轻量级的基于深度学习的模型。我们利用标签平滑技术与软共识（LSSC）分布一起插入多知识的模型的训练过程。我们引入平均余弦相似性度量（ACS）来量化由LSSC模型生成的催眠密度图与由评分者共识生成的催眠密度图之间的相似性。当我们用LSSC训练模型时，模型的性能在所有数据库上都有所提高。我们发现，在用LSSC训练的模型生成的催眠密度图和共识生成的催眠密度图之间，ACS增加（高达6.4%）。我们的方法绝对能够使模型更好地适应评分者群体的共识。未来的工作将集中在不同的评分架构，并希望大规模异构的多得分数据集的进一步调查。
Inter-scorer variability in scoring polysomnograms is a well-known problem. Most of the existing automated sleep scoring systems are trained using labels annotated by a single-scorer, whose subjective evaluation is transferred to the model. When annotations from two or more scorers are available, the scoring models are usually trained on the scorer consensus. The averaged scorer’s subjectivity is transferred into the model, losing information about the internal variability among different scorers. In this study, we aim to insert the multiple-knowledge of the different physicians into the training procedure. The goal is to optimize a model training, exploiting the full information that can be extracted from the consensus of a group of scorers. We train two lightweight deep learning-based models on three different multi-scored databases. We exploit the label smoothing technique together with a soft-consensus (LSSC) distribution to insert the multiple-knowledge in the training procedure of the model. We introduce the averaged cosine similarity metric (ACS) to quantify the similarity between the hypnodensity-graph generated by the models with-LSSC and the hypnodensity-graph generated by the scorer consensus. The performance of the models improves on all the databases when we train the models with our LSSC. We found an increase in ACS (up to 6.4%) between the hypnodensity-graph generated by the models trained with-LSSC and the hypnodensity-graph generated by the consensus. Our approach definitely enables a model to better adapt to the consensus of the group of scorers. Future work will focus on further investigations on different scoring architectures and hopefully large-scale-heterogeneous multi-scored datasets.
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DOI：
10.1093/jamia/ocy131
发表时间：
2018-12-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Biswal S;Sun H;Goparaju B;Westover MB;Sun J;Bianchi MT
通讯作者：
Bianchi MT
影响因子：
5.6
作者：
Patanaik A;Ong JL;Gooley JJ;Ancoli-Israel S;Chee MWL
通讯作者：
Chee MWL
DOI：
10.1109/tnsre.2021.3117970
发表时间：
2021-01-01
影响因子：
4.9
作者：
Fiorillo, Luigi;Favaro, Paolo;Faraci, Francesca Dalia
通讯作者：
Faraci, Francesca Dalia
DOI：
10.1109/tnsre.2019.2896659
发表时间：
2019-03-01
影响因子：
4.9
作者：
Huy Phan;Andreotti, Fernando;De Vos, Maarten
通讯作者：
De Vos, Maarten
影响因子：
5.6
作者：
Abou Jaoude, Maurice;Sun, Haoqi;Lam, Alice D.
通讯作者：
Lam, Alice D.