  Medical imaging data suffers from the limited availability of annotation
because annotating 3D medical data is a time-consuming and expensive task.
Moreover, even if the annotation is available, supervised learning-based
approaches suffer highly imbalanced data. Most of the scans during the
screening are from normal subjects, but there are also large variations in
abnormal cases. To address these issues, recently, unsupervised deep anomaly
detection methods that train the model on large-sized normal scans and detect
abnormal scans by calculating reconstruction error have been reported. In this
paper, we propose a novel self-supervised learning technique for anomaly
detection. Our architecture largely consists of two parts: 1) Reconstruction
and 2) predicting geometric transformations. By training the network to predict
geometric transformations, the model could learn better image features and
distribution of normal scans. In the test time, the geometric transformation
predictor can assign the anomaly score by calculating the error between
geometric transformation and prediction. Moreover, we further use
self-supervised learning with context restoration for pretraining our model. By
comparative experiments on clinical brain CT scans, the effectiveness of the
proposed method has been verified.
