Skeletal Age Assessment Method Based on Convolutional Neural Network and Multiple Attention Mechanism

Abstract

The invention discloses a bone age assessment method based on a convolutional neural network and a multiple attention mechanism, including: in the training stage, the backbone network input is a metacarpal bone image, a feature map F is obtained through a feature extractor, and then a bone age regression value is obtained; multiple The input of the attention module is the feature map F, and M sub-attention maps are obtained through the compression operation and the attention map splitting operation, and each sub-attention map is multiplied by the feature map F to obtain the corresponding bone age regression value; combined with the backbone network and multiple The bone age regression value obtained by the attention module is used to train the neural network with a multi-task learning strategy; in the test phase, the metacarpal image to be tested is input into the trained neural network, and the bone age evaluation value is obtained through the backbone network. The above model can be trained end-to-end; at the same time, it can automatically generate an attention distribution map, which has better generalization; in addition, based on a 2D convolutional neural network, it is fast and accurate, and the average evaluation error is within 4.1 months.

Description

technical field [0001] The invention relates to the technical field of intelligent medical image analysis, in particular to a bone age assessment method based on a convolutional neural network and a multiple attention mechanism. Background technique [0002] The traditional bone age assessment usually takes X-rays of the subject's left palm and wrist, and then conducts bone age assessment with the help of common standards. This process relies heavily on the practitioner's experience and is also time-consuming. In addition, there are great differences in skeletal development under different races, climates and other conditions, so the corresponding standards also vary widely, which increases the complexity of bone age assessment. [0003] In order to speed up the evaluation, improve the accuracy of the evaluation and reduce the work intensity, a computer-aided system (CAD) based on artificial intelligence came into being, and achieved a higher accuracy than human experts in ...

AI Technical Summary

Problems solved by technology

However, most of these artificial intelligence technologies introduce fine-grained region-of-interest annotations, and focus on specific bone parts as regions of interest (such as wrist bones, proximal phalanges, etc.) through detection and segmentation methods, which brings expensive annotations. Cost and model complexity limit the practical application value of the method

Images