Before revision:  We propose to use textual findings , which are routinely written by clinicians during manual image analysis, to help overcome this problem. The key idea is to use a contrastive loss to train image and text feature extractors to recognize if a given image-finding pair is a true match. The learned image feature extractor is then fine-tuned, in a transfer learning setting , for a supervised classification task .
After revision:  We propose to use textual findings , which are often readily available in medical records, contain rich but unstructured interpretations written by experts as part of standard clinical practice. We propose using these textual reports as a form of weak supervision to improve the image interpretation performance of a neural network without requiring additional manually labeled examples. We use an image-text matching task to train a feature extractor and then fine-tune it in a transfer learning setting , for a supervised classification task .