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Despite tremendous progress in computer vision, there has not been an attemptfor machine learning on very large-scale medical image databases. We present aninterleaved text/image deep learning system to extract and mine the semanticinteractions of radiology images and reports from a national researchhospital's Picture Archiving and Communication System. With natural languageprocessing, we mine a collection of representative ~216K two-dimensional keyimages selected by clinicians for diagnostic reference, and match the imageswith their descriptions in an automated manner. Our system interleaves betweenunsupervised learning and supervised learning on document- and sentence-leveltext collections, to generate semantic labels and to predict them given animage. Given an image of a patient scan, semantic topics in radiology levelsare predicted, and associated key-words are generated. Also, a number offrequent disease types are detected as present or absent, to provide morespecific interpretation of a patient scan. This shows the potential oflarge-scale learning and prediction in electronic patient records available inmost modern clinical institutions.

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation