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Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders  
[PERSON] 1,*(c), [PERSON] 2 and [PERSON] 1  
Footnote 120: https:approach has several disadvantages. First of all, it takes a long time to assess bone age, and it also requires professional medical personnel to evaluate. Secondly, due to manual evaluation, there are human errors. In the TW method, 20 bones are scored separately, these scores are then combined to obtain a composite score, which is then mapped to a table to calculate bone age. Since bone age is determined by scoring, a possible problem is that an overly simplistic scoring method may lead to inaccurate estimates of bone age. Since the above disadvantages lie in traditional bone age assessment methods, in the past, automatic bone age assessment systems have been proposed in many studies, while due to the development of deep learning technology, more and more systems for medical purposes are based on deep learning, including medical human-computer interfaces, medical diagnosis systems, medical data analysis, medical registration systems, etc. These various applications are increasingly inseparable from deep learning. For automatic bone age assessment systems, many studies also build their systems based on CNN. In these studies, the way of estimating bone age can be divided into two types. First, directly use CNNs to build a regression model for bone age [4, 5], since this method is to fit all bone ages of palm bone X-ray by one CNN, in this case, the main problem is that if the estimation is wrong, it is easy to have great errors. Another way is to divide the palm bone X-ray into several stages according to the bone age [6, 7]. At this time, the CNNs are dealing with the classification problem, however, the main problem of this method is that if the stages are divided too coarsely, the estimation of bone age will be not accurate enough. If the stages are divided too finely, the difference between the stages will be not obvious, which will cause it to be difficult to fit by CNNs and prone to overfitting. In order to avoid these problems, a compromise solution can be considered. First, the palm bone X-rays are divided into several rough stages, and for each stage an independent regression model is trained to estimate the bone age. Therefore, the accuracy of classification of the rough stages is very important. In this paper, we mainly deal with the problem of data imbalance in the classification at the coarse stage, and use the classification results to the CNN regression network to calculate bone age. Although many existing bone age studies have trained a large number of palm bone X-ray databases [8], which has led to good results on classification, however, due to differences in the growth process of children, due to differences in races, diets, and different growth environments, the development process of children varies in different regions. Therefore, it is necessary for each region to build its own bone age estimation system. For this reason, this paper is based on the palm bone X-ray films taken by children in Taiwan to build a system. In this case, we consider a problem: it will take a long time for X-ray data to accumulate to a sufficient number. Especially in toddler and post-puberty stages, there are few outpatient cases, so the data at these two stages will be greatly lacking. In the initial research stage of the bone age system, there will be a problem of very unbalanced data. For example, in this paper, in the three stages we use, the proportion of the database we collected in females is approximately 5:90:5, which is a very biased data distribution. In our follow-up experiments, we will find that when training directly with CNNs all the classification results are in the puberty stage. This kind of X-ray data imbalance problem often occurs.