Combination of Active Transfer Learning and Natural Language Processing to Improve Liver Volumetry Using Surrogate Metrics with Deep Learning
Combination of Active Transfer Learning and Natural Language Processing to Improve Liver Volumetry Using Surrogate Metrics with Deep Learning
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DOI：
10.1148/ryai.2019180019
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发表时间：
2019-01-01
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影响因子：
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通讯作者：
Oermann, Eric K.
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作者：
Marinelli, Brett;Kang, Martin;Oermann, Eric K.
目的：确定具有替代指标和积极转移学习的弱监督学习是否可以加速深度学习模型的临床部署。材料和方法：通过利用肝脏肿瘤细分（LITS）挑战2017年公共数据（n = 131个研究），自然语言处理，自然语言处理在2014年1月1日至2016年12月31日之间获得的239个回顾性收集的门户静脉相腹部CT研究中，对报告和一种主动学习方法进行了培训，可追溯到239个追溯收集的门静脉相腹部CT研究。用于指导积极学习和评估准确性。评估了基于该模型（n = 34例）与放射学报告和终阶段肝病的模型预测的肝量（MELD-NA）评分模型的总生存期。使用配对的学生T检验，平淡的Altman分析和类内相关性比较绝对肝脏体积的差异。使用Kaplan-Meier方法和Mantel-Cox测试进行生存分析。回报：肝脏体积预测较差的患者的数据（n = 10），只有使用公开数据训练的模型将其纳入了一种活跃的学习方法中，该方法训练有素训练一个新的模型（LITS数据以及低估和低估的主动学习案例[LITS-OU]），在持有的机构测试集（绝对体积差异为231 vs 176 ml，p = .0005）上表现出色。在整体生存分析中，使用最佳主动学习训练训练的模型（LITS-O）预测的肝量至少与从放射学报告中提取的肝量和预测生存中提取的肝量相当。结论：结论：使用替代指标促进的替代指标的主动转移学习在主要的肝移植中心的临床有意义的肝脏分割的深度学习模型。 （c）RSNA，2019年
Purpose: To determine if weakly supervised learning with surrogate metrics and active transfer learning can hasten clinical deployment of deep learning models.Materials and Methods: By leveraging Liver Tumor Segmentation (LiTS) challenge 2017 public data (n = 131 studies), natural language processing of reports, and an active learning method, a model was trained to segment livers on 239 retrospectively collected portal venous phase abdominal CT studies obtained between January 1, 2014, and December 31, 2016. Absolute volume differences between predicted and originally reported liver volumes were used to guide active learning and assess accuracy. Overall survival based on liver volumes predicted by this model (n = 34 patients) versus radiology reports and Model for End-Stage Liver Disease with sodium (MELD-Na) scores was assessed. Differences in absolute liver volume were compared by using the paired Student t test, Bland-Altman analysis, and intraclass correlation; survival analysis was performed with the Kaplan-Meier method and a Mantel-Cox test.Results: Data from patients with poor liver volume prediction (n = 10) with a model trained only with publicly available data were incorporated into an active learning method that trained a new model (LiTS data plus over- and underestimated active learning cases [LiTS-OU]) that performed significantly better on a held-out institutional test set (absolute volume difference of 231 vs 176 mL, P =.0005). In overall survival analysis, predicted liver volumes using the best active learning-trained model (LiTS-OU) were at least comparable with liver volumes extracted from radiology reports and MELD-Na scores in predicting survival.Conclusion: Active transfer learning using surrogate metrics facilitated deployment of deep learning models for clinically meaningful liver segmentation at a major liver transplant center. (C) RSNA, 2019