We demonstrate a classification approach for MRI image-quality based on deep auto-encoder that can be trained with samples coming from only one class (eg. only good image-quality). This approach is helpful in situations where class-imbalance is unavoidable (i.e. it is easy to obtain a large number of image samples from one class but very difficult to obtain similar number of samples from other class). Our approach shows excellent accuracy in binary classification with AUC of 0.975 in identifying MRI images of good & bad quality in clinical practice from several sites.
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