Anomaly detection in chest radiographs with a weakly supervised flow-based deep learning method

Preventing the oversight of anomalies in chest X-ray radiographs (CXRs) during diagnosis is a crucial issue. Deep learning (DL)-based anomaly detection methods are rapidly growing in popularity, and provide effective solutions to the problem, but the workload in labeling CXRs during the training procedure remains heavy. To reduce the workload, a novel anomaly detection method for CXRs based on weakly supervised DL is presented in this study. The DL is based on a flow-based deep neural network (DNN) framework with which two normality metrics (logarithm likelihood and logarithm likelihood ratio) can be calculated. With this method, only one set of normal CXRs requires labeling to train the DNN, then the normality of any unknown CXR can be evaluated. The area under the receiver operation characteristic curve acquired with the logarithm likelihood ratio metric ($\approx0.783$) was greater than that obtained with the logarithm likelihood metric, and was a value comparable to those in previous studies where other weakly supervised DNNs were implemented.