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

• URL: http://arxiv.org/abs/2212.07582v1
• Date: Thu, 15 Dec 2022 02:05:12 GMT
• Title: Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training
• Authors: Yusuke Akamatsu, Yoshifumi Onishi, Hitoshi Imaoka, Junko Kameyama, Hideo Tsurushima
• Abstract summary: 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.
• Score: 3.8323580808203785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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