Before revision: When training deep neural networks for medical image classification, obtaining a sufficient number of manually annotated images is often a significant challenge. We propose to use textual findings , which are routinely written by clinicians during manual image analysis, to help overcome this problem.
After revision: When training deep neural networks for medical image classification, obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports , which are routinely written by clinicians during manual image analysis, to help overcome this problem.