论文标题
通过域适应对医学图像的深度质量评估
Deep-based quality assessment of medical images through domain adaptation
论文作者
论文摘要
在不同的领域通常需要预测多媒体内容的质量。在某些应用中，质量指标至关重要，影响很大，并且可能会影响决策，例如医疗多媒体的诊断。在本文中，我们通过提出一个有效且浅的模型来预测医学图像的质量而无需参考少量带注释的数据，将重点放在此类应用上。我们的模型基于卷积自我发挥，旨在从图像的相关局部特征中对复杂表示形式进行建模，该图像本身会滑过图像以插入全球质量得分。我们还以无监督和半监督的方式应用领域适应学习。通过由几个图像及其相应的主观分数组成的数据集评估所提出的模型。获得的结果表明了所提出的方法的效率，但也显示了应用域适应以概括有关感知质量预测下游任务的不同多媒体域的相关性。 \ footNote {由TIC-ART项目资助，区域基金（区域中心 - 洛伊尔地区）}}}
Predicting the quality of multimedia content is often needed in different fields. In some applications, quality metrics are crucial with a high impact, and can affect decision making such as diagnosis from medical multimedia. In this paper, we focus on such applications by proposing an efficient and shallow model for predicting the quality of medical images without reference from a small amount of annotated data. Our model is based on convolution self-attention that aims to model complex representation from relevant local characteristics of images, which itself slide over the image to interpolate the global quality score. We also apply domain adaptation learning in unsupervised and semi-supervised manner. The proposed model is evaluated through a dataset composed of several images and their corresponding subjective scores. The obtained results showed the efficiency of the proposed method, but also, the relevance of the applying domain adaptation to generalize over different multimedia domains regarding the downstream task of perceptual quality prediction. \footnote{Funded by the TIC-ART project, Regional fund (Region Centre-Val de Loire)}
