Improved Multi-task Learning for Detection of Calcification Regions in Dental Panoramic Radiographs

We can find areas of calcification in Carotid arteries on dental panoramic radiographs, which may be a sign of vascular disease. It is hoped that detection of these areas can prevent the sudden onset of vascular disorders by prompting patients to consult a physician. For this purpose, we propose an automatic detection method based on a model combining semantic segmentation and convolutional neural networks. This method uses Nash-MTL, a multi-task learning method, to solve the tradeoff between segmentation and identification tasks with different properties. In this method, the parameters for adjusting the gradient vector are obtained through an optimization method. However, in the case of two tasks, as in this problem, the exact solution can be obtained, computational complexity can be reduced without compromising accuracy. Furthermore, the computational complexity can be further reduced by simplifying the solution. The effectiveness of the proposed method is demonstrated by applying it to actual data.