  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.
