Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database

CVPR 2015 Hoo-Chang ShinLe LuLauren KimAri SeffJianhua YaoRonald M. Summers

Despite tremendous progress in computer vision, effective learning on very large-scale (>100K patients) medical image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system... (read more)

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