Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders

Abstract

Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages, respectively. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder which produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, we also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network.