计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 221200107-6.doi: 10.11896/jsjkx.221200107
林毅, 周芃, 陈彦明
LIN Yi,ZHOU Peng, CHEN Yanming
摘要: 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。
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