Body Part and Imaging Modality Classification for a General Radiology Cognitive Assistant

摘要

Decision support systems built for radiologists need to cover a fairly wide range of image types, with the abilityto route each image to the relevant algorithm. Furthermore, the training of such networks requires buildinglarge datasets with significant efforts in image curation. In situations where the DICOM tag of an image isunavailable, or unreliable, a classifier that can automatically detect the body part depicted in the image, aswell as the imaging modality, is necessary. Previous work has shown the use of imaging and textual features todistinguish between imaging modalities. In this work, we present a model for the simultaneous classification ofbody part and imaging modality, which to our knowledge has not been done before, as part of the larger work tocreate a cognitive assistant for radiologists. This classification network consists of 10 classes built from a VGGnetwork architecture using transfer learning to learn generic features. An accuracy of 94.8% is achieved.