Edema is a common symptom of kidney disease, and quantitative measurement of edema is desired. This paper presents a method to estimate the degree of edema from facial images taken before and after dialysis of renal failure patients. As tasks to estimate the degree of edema, we perform pre- and post-dialysis classification and body weight prediction. We develop a multi-patient pre-training framework for acquiring knowledge of edema and transfer the pre-trained model to a model for each patient. For effective pre-training, we propose a novel contrastive representation learning, called weight-aware supervised momentum contrast (WeightSupMoCo). WeightSupMoCo aims to make feature representations of facial images closer in similarity of patient weight when the pre- and post-dialysis labels are the same. Experimental results show that our pre-training approach improves the accuracy of pre- and post-dialysis classification by 15.1% and reduces the mean absolute error of weight prediction by 0.243 kg compared with training from scratch. The proposed method accurately estimate the degree of edema from facial images; our edema estimation system could thus be beneficial to dialysis patients.

翻译：肾脏疾病是肾脏疾病的一种常见症状,需要对水肿进行定量测量。本文件介绍了一种方法,用以估计通过对肾衰竭病人进行透析前后的面部图像产生的水肿程度。作为评估水肿程度的任务,我们进行透析前和透析后分类和体重预测。我们开发了一个多病人前培训框架,以获取对水肿的了解,并将预先培训的模式转换为每个病人的模型。为了进行有效的训练前,我们建议进行新的对比性介绍学习,称为体重觉察力监测的势头对比(WeightSupMoco)。我们透析前和透析后标签相同时,透析后标签的目的是使面部图像的特征表现更加接近病人体重的相似性。实验结果表明,我们的培训前方法提高了水肿前和透析后分类的准确性,提高了15.1%,并将体重预测的绝对偏差减少了0.243公斤,而从零开始的培训则减少了。拟议的方法准确地估计了面部图像中的水肿程度;因此,我们的水肿估计系统可能对诊断病人有益。