AI 中文总结
研究利用深度学习，通过带加法注意力的 EfficientNet 及超过 12000 张 RSNA 数据集的 X 光图像，经预处理和卷积神经网络训练预测小儿骨龄，使用两个模型变体对比，结果显示该方法更准确有效，为小儿内分泌疾病诊断提供新方案。
AI 中文摘要
小儿骨龄预测在临床实践中至关重要，能辅助诊断内分泌失调并洞察儿童生长发育情况。传统方法 labor-intensive 且需专业放射学知识。本文提出基于深度学习的方法，使用带加法注意力的 EfficientNet，利用超过 12000 张 RSNA 骨龄数据集的 X 光图像，经预处理转化为三通道图像，训练卷积神经网络自动学习手部骨骼图像特征。使用 EfficientNet 模型的两个变体（B0 和 B4），其中 EfficientNetB4 还用加法注意力机制微调。通过曲线展示对比，结果表明在多数情况下，EfficientNetB4 及其带加法注意力的变体预测骨龄更准确，性能优于 EfficientNetB0，并给出具体性能指标。训练和验证损失的学习曲线证实有效学习未过拟合或欠拟合，进一步验证该方法在小儿内分泌疾病诊断中的有效性。
英文摘要
Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child's growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach's efficacy in pediatric endocrine disease diagnosis.
CommentsPublished in 2023 26th International Conference on Computer and Information Technology (ICCIT), IEEE, December 2023
DOI：10.1109/ICCIT60459.2023.10441258
