  With the development of medical imaging technology, medical images have
become an important basis for doctors to diagnose patients. The brain structure
in the collected data is complicated, thence, doctors are required to spend
plentiful energy when diagnosing brain abnormalities. Aiming at the imbalance
of brain tumor data and the rare amount of labeled data, we propose an
innovative brain tumor abnormality detection algorithm. The semi-supervised
anomaly detection model is proposed in which only healthy (normal) brain images
are trained. Model capture the common pattern of the normal images in the
training process and detect anomalies based on the reconstruction error of
latent space. Furthermore, the method first uses singular value to constrain
the latent space and jointly optimizes the image space through multiple loss
functions, which make normal samples and abnormal samples more separable in the
feature-level. This paper utilizes BraTS, HCP, MNIST, and CIFAR-10 datasets to
comprehensively evaluate the effectiveness and practicability. Extensive
experiments on intra- and cross-dataset tests prove that our semi-supervised
method achieves outperforms or comparable results to state-of-the-art
supervised techniques.
