A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs
A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs
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DOI：
10.1007/s10266-021-00615-2
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发表时间：
2021-05-23
期刊：
影响因子：
2.5
通讯作者：
Ariji, Eiichiro
中科院分区：
文献类型：
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作者：
Mori, Mizuho;Ariji, Yoshiko;Ariji, Eiichiro
研究将深度学习源模型从一个机构（机构a）应用到另一个机构（机构B），以创建有效的模型（目标模型），用于在全景x线片上检测上颌鼻窦和诊断上颌鼻窦炎。此外，为迁移学习确定合适的训练数据量。源模型使用来自A机构的350张全景x光片作为训练数据。通过将25、50、100、150或225张全景x光片作为训练数据从机构B添加到源模型中进行迁移学习；这产生了目标型号T25， T50, T100， T150和T225。使用A机构40张图像和B机构30张图像的测试数据对每个模型进行评价。源模型检测上颌窦的性能指标（召回率、精度和F1评分）在使用A机构测试数据A时均超过0.98，而在使用B机构测试数据B时则有所下降。在使用B测试数据对目标模型进行评价时，T25模型的检测性能有所提高（召回率为0.967）。T50模型对上颌窦炎的诊断灵敏度超过0.9。迁移学习将少量数据应用到源模型中，在全景x线片上颌鼻窦检测和上颌鼻窦炎诊断中取得了较高的性能。本研究可为其他机构采用源模型提供参考。
To investigate the use of transfer learning when applying a deep learning source model from one institution (institution A) to another institution (institution B) for creating effective models (target models) for the detection of maxillary sinuses and diagnosis of maxillary sinusitis on panoramic radiographs. In addition, to determine appropriate numbers of training data for the transfer learning. Source model was created using 350 panoramic radiographs from institution A as training data. Transfer learning was performed by adding 25, 50, 100, 150, or 225 panoramic radiographs as training data from institution B to the source model; this yielded the target models T25, T50, T100, T150 and T225. Each model was then evaluated using test data that comprised 40 images from institution A, 30 images from institution B. The performance indices (recall, precision and F1 score) for detecting the maxillary sinuses by the source model exceeded 0.98 when using test data A from institution A, but they deteriorated when using test data B from institution B. In the evaluation of target models using test data B, model T25 showed improved detection performance (recall of 0.967). The diagnostic performance of model T50 for maxillary sinusitis exceeded 0.9 in sensitivity. Transfer learning, which involves applying a small amount of data to the source model, yielded high performances in detecting the maxillary sinuses and diagnosing the maxillary sinusitis on panoramic radiographs. This study serves as a reference when adapting source models to other institutions.