Positional Multi-Cross-Attention for Bone Age Estimation Using Deep Multiple Instance Learning

The bone age estimation is clinically important as it can be used by physicians to read and report medial images such as bone scintigraphy considering age-related bone changes. Some recent research has applied deep learning techniques to assess bone age and have achieved positive results. Whole-body bone scintigraphy usually contains millions of pixels, which is computationally infeasible. This problem can be solved effectively with multiple instance learning by treating a whole image as a bag of instances. However, many approaches usually assume that there are no dependencies or ordering among instances. In the present study, we propose a multiple instance learning based method for bone age prediction using whole-body bone scan images. Our network architecture combines attention-based multiple instances learning with a multi-cross attention module to handle large input images and dependencies among instances. The experiments conducted on the Chonnam National University Hwasun Hospital data set have shown the potential performance of our model. The proposed framework can be used as a robust support tool for clinicians to analyze and prognosticate bone aging and age-related diseases.