基于语义注意力的医学图像超分辨率方法

Medical Image Super-resolution Method Based on Semantic Attention

在医学图像领域,清晰的医学图像能够帮助医生更好地诊断疾病。然而,由于受到成像设备的限制,生成的医学图像往往分辨率较低并可能影响后期诊断。因此,使用超分辨率方法提高图像的分辨率显得尤为重要。近些年来,随着深度学习的发展,基于深度学习的自然图像超分辨率方法被广泛研究,并取得了一定效果。然而,不同于自然图像超分辨率,医学图像超分辨率往往是为下游医学任务服务。许多下游医学任务,例如疾病诊断、语义分割等等,往往会对某些区域感兴趣。但是传统图像超分辨率方法往往平等地对待图像中所有区域,没有考虑到感兴趣区域对于下游医学任务的重要性。针对此问题,提出了一种基于语义注意力的医学图像超分辨率方法。该注意力机制通过加权方式对图像中感兴趣区域进行额外关注,从而使得超分辨率图像更有助于下游医学任务。该方法在新冠肺炎数据集COVID_19和胃肠息肉数据集Kvasir-SEG上都取得了领先于其他主流超分辨率方法的效果。

In the field of medical images processing,clear medical images can help doctors to diagnose diseases better.However,due to the limitations of imaging equipment,the generated medical images are often of low resolution and thus may be in appro-priate for diagnosis.Therefore,it is very important to use super-resolution method to improve the image resolution.In recent years,with the development of deep learning,natural image super-resolution methods based on deep learning have been widely studied and achieved promising performance.However,unlike natural image super-resolution,medical image super-resolution often serves downstream medical tasks.The downstream medical tasks,such as disease diagnosis and semantic segmentation,tend to be of interest to certain regions.However,traditional image super-resolution methods often tend to treat all regions in the image equally,without considering the importance of the regions of interest for downstream medical tasks.To tackle this problem,this paper proposes a medical image super-resolution method based on semantic attention.The semantic attention module pays extra attention to the regions of interest in the image by weighting,so that the super-resolution image is more helpful for downstream medical tasks.Experimental results show that the proposed method outperforms other mainstream super-resolution methods on COVID-19 dataset and gastrointestinal polyps dataset Kvasir-SEG.