CheXzero: Detect Pathologies From Unannotated X-ray Images

This article was published as a part of the Data Science Blogathon. Working on a task involving the interpretation of chest X-ray medical images and no labeled data at your disposal? Researchers from Harvard Medical School and Stanford University have devised an artificial intelligence diagnostic tool that can detect diseases from natural language descriptions of chest X-rays without needing the labeled data. This is a major step toward significant advancement in clinical AI design because most existing models require vast amounts of annotated data before that data can be fed into a model for training. This research paper will look at the proposed method in further detail.