Unsupervised Deep Anomaly Detection in Chest Radiographs.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.

Pilot study of eruption forecasting with muography using convolutional neural network.

Muography is a novel method of visualizing the internal structures of active volcanoes by using high-energy near-horizontally arriving cosmic muons. The purpose of this study is to show the feasibility of muography to forecast the eruption event with the aid of the convolutional neural network (CNN). In this study, seven daily consecutive muographic images were fed into the CNN to compute the probability of eruptions on the eighth day, and our CNN model was trained by hyperparameter tuning with the Bayesian optimization algorithm.

Hospitalization period and direct medical cost in patients using warfarin or novel oral anti-coagulants after a cerebral embolism.

Background Warfarin has been used in Japan for a long time in patients after cerebral embolism to prevent recurrence. Recently, several novel oral anti-coagulants (NOACs) have been approved for use and are gradually replacing warfarin. However, it remains unclear whether warfarin and other NOACs differ from each other with respect to drug costs and length of stay (LOS) during treatment in Japan.