基于卷积神经网络的牙科x射线伪影预测模型中训练样本大小对正则化效果的影响

IF 0.2 Q4 BIOLOGY
Adam Adli, P. Tyrrell
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引用次数: 0

摘要

计算机的进步使得越来越先进的机器学习模型的实际应用能够帮助医疗保健提供者进行医学图像的诊断和检查。通常,缺乏训练数据和计算时间可能是在医学成像领域开发准确机器学习模型的限制因素。作为一种可能的解决方案,本研究调查了L2正则化是否会缓和由于训练样本量小而导致的过拟合。方法:本研究采用迁移学习实验对牙科x射线二分类模型进行研究,探讨五种常见卷积神经网络架构中L2正则化与训练样本大小的关系。研究了模型的测试性能,描述了技术实现的细节,包括计算次数和硬件考虑,以及性能因素和实际可行性。结果:实验结果显示,较小的训练样本量比较大的训练样本量从正则化中获益更多。此外,结果表明,应用L2正则化不会产生显著的计算开销,并且当训练样本量相对较小时,额外的训练L2正则化轮数是可行的。结论:总的来说,本研究发现,相对于训练样本量,采用正则化的好处可能是最具成本效益的。建议在形成可实现的泛化性改进的期望时,应仔细考虑训练样本的大小,这些改进是由于将计算资源投入模型正则化而产生的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Training Sample Size on the Effects of Regularization in a Convolutional Neural Network-based Dental X-ray Artifact Prediction Model
Introduction: Advances in computers have allowed for the practical application of increasingly advanced machine learning models to aid healthcare providers with diagnosis and inspection of medical images. Often, a lack of training data and computation time can be a limiting factor in the development of an accurate machine learning model in the domain of medical imaging. As a possible solution, this study investigated whether L2 regularization moderate s the overfitting that occurs as a result of small training sample sizes.Methods: This study employed transfer learning experiments on a dental x-ray binary classification model to explore L2 regularization with respect to training sample size in five common convolutional neural network architectures. Model testing performance was investigated and technical implementation details including computation times and hardware considerations as well as performance factors and practical feasibility were described.Results: The experimental results showed a trend that smaller training sample sizes benefitted more from regularization than larger training sample sizes. Further, the results showed that applying L2 regularization did not apply significant computational overhead and that the extra rounds of training L2 regularization were feasible when training sample sizes are relatively small.Conclusion: Overall, this study found that there is a window of opportunity in which the benefits of employing regularization can be most cost-effective relative to training sample size. It is recommended that training sample size should be carefully considered when forming expectations of achievable generalizability improvements that result from investing computational resources into model regularization.
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