Attention mechanism-based deep learning method for hairline fracture detection in hand X-rays.

Wrist and finger fractures detection is always the weak point of associate study, because there are small targets in X-rays, such as hairline fractures. In this paper, a dataset, consisting of 4346 anteroposterior, lateral and oblique hand X-rays, is built from many orthopedic cases. Specifically, it contains a lot of hairline fractures. An automatic preprocessing based on generative adversative network (GAN) and a detection network, called WrisNet, are designed to improve the detection performance of wrist and finger fractures. In the preprocessing, an attention mechanism-based GAN is proposed for obtaining the approximation of manual windowing enhancement. A multiscale attention-module-based generator of the GAN is proposed to increase continuity between pixels. The discriminator and the generator can achieve 93% structural similarity (SSIM) as manual windowing enhancement without manual parameter adjustment. The designed WrisNet is composed of two components: a feature extraction module and a detection module. A group convolution and a lightweight but efficient triplet attention mechanism are elaborately embedded into the feature extraction module, resulting in richer representations of hairline fractures. To obtain more accurate locating information in this condition, the soft non-maximum suppression algorithm is employed as the post-processing method of the detection module. As shown in experimental results, the designed method can have obvious average precision (AP) improvement up to 7% or more than other mainstream frameworks. The automatic preprocessing and the detection net can greatly reduce the degree of artificial intervention, so it is easy to be implemented in real clinical environment.