Meeting Banner
Abstract #4849

A deep autoencoder method for image quality assessment

Andre Maximo1, Chitresh Bhushan2, Desmond T.B. Yeo2, and Thomas K Foo2

1GE Healthcare, Rio de Janeiro, Brazil, 2GE Global Research, Niskayuna, NY, United States

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here