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

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.

Sleep/wake classification via remote PPG signals.

This paper proposes a remote sleep/wake classification method by combining vision-based heart rate (HR) estimation and convolutional neural network (CNN). Instead of inputting the estimated HR with low temporal resolution, remote PPG (Photoplethysmogram) signals, which contain high-temporal-resolution HR information, are input into the CNN. To reduce noise in the remote PPG signals, we propose a dynamic HR filter. Evaluation results show that the dynamic HR filter works more effectively in comparison with the static filter, which helps improve the area under the ROC curve (AUC) to 0.70, which is almost as good as the reference 0.71 for HR from a wearable sensor.

A neural-network-based investigation of eye-related movements for accurate drowsiness estimation.

Many studies reported that eye-related movements, e.g., blank stares, blinking and drooping eyelids, are highly indicative symptoms of drowsiness. However, few researchers have investigated the computational efficacy accounted for drowsiness estimation by these eye-related movements. This paper thus analyzes two typical eye-related movements, i.e., eyelid movements Xel(t) and eyeball movements Xeb(t), and investigates neural-network-based approaches to model temporal correlations. Specifically, we compare the effectiveness of three combinations of eye-related movements, i.e., [Xel(t)], [Xeb(t)], and [Xel(t),Xeb(t)], for drowsiness estimation. Furthermore, we investigate the usefulness of two typical types of neural networks, i.e., CNN-Net and CNNLSTM-Net, for better drowsiness modeling. The experimental results show that [Xel(t),Xeb(t)] can achieve a better performance than [Xel(t)] for short time drowsiness estimation while [Xeb(t)]alone performs worse even than the baseline method (PERCLOS). In addition, we found that CNN-Net are more effective for accurate drowsiness level modeling than CNNLSTM-Net.

Autoimmune pancreatitis accompanied with recurrence of bladder cancer: difficulty in diagnosis and management of systemic lesions in a case with autoimmune pancreatitis.

CONTEXT: A case of autoimmune pancreatitis develops a hepatic metastasis of bladder cancer resected over 5 years before, mimicking a pseudotumor of the liver. CASE REPORT: A 71-year-old man with a surgical history of bladder cancer (pT4, G2>3, N (+)) later developed autoimmune pancreatitis. Diagnosis of autoimmune pancreatitis was not problematic; however, a variety of systemic disorders appeared after the onset of autoimmune pancreatitis, possibly associated with autoimmune disorder or steroid therapy. These included pancreatic stone attack, septic shock due to ureteral stenosis, and bloody phlegm due to a lung aspergilloma. These events were not easily controlled but were managed with clinical efforts. In the following course, pelvic lymph nodes gradually enlarged and a hepatic mass occurred at 5 years and 6 months after total cystectomy. Several candidates were considered for the hepatic lesion including inflammatory pseudotumor, cholangiocellular carcinoma, and hepatic adenoma. However, percutaneous biopsy confirmed metastasis of the bladder cancer. In general, recurrence after 5 years following cystectomy is extremely rare in cases of pT4 bladder cancer with lymph node metastasis. CONCLUSIONS: Patients of autoimmune pancreatitis display various problematic scenarios in diagnosis and long-term management, not only for their pancreatic lesions but also for systemic lesions.