Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training

TL;DR

This paper introduces a novel contrastive learning framework, WeightSupMoCo, for estimating edema from facial images of dialysis patients, improving classification and weight prediction accuracy.

Contribution

It proposes a multi-patient pre-training method with a new contrastive learning approach, WeightSupMoCo, for better edema estimation from facial images.

Findings

Pre-training improves classification accuracy by 15.1%.

Reduces mean absolute error of weight prediction by 0.243 kg.

Effective edema estimation from facial images demonstrated.

Abstract

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.