论文标题
透析前后通过对比度多患者预训练之前和之后拍摄的面部图像的水肿估计
Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training
论文作者
论文摘要
水肿是肾脏疾病的常见症状，需要对水肿进行定量测量。本文提出了一种估计肾衰竭患者透析之前和之后从面部图像中估算水肿程度的方法。作为估计水肿程度的任务，我们执行透析前后的分类和体重预测。我们开发了一个多人预训练框架，用于获取水肿知识并将预训练的模型转移到每个患者的模型中。为了进行有效的预训练，我们提出了一种新颖的对比表示学习，称为体重感知的监督动量对比度（strigeSupmoco）。当透析前和透析后标签相同时，WogeSupmoco的目的是使患者体重的相似性与患者体重相似，使面部图像的特征表示。实验结果表明，我们的训练前方法将前后分类前后分类的准确性提高了15.1％，而与SCRATCH的培训相比，平均体重预测的平均绝对误差降低了0.243 kg。提出的方法准确地估算了面部图像的水肿程度；因此，我们的水肿估计系统可能对透析患者有益。
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.
