Saahil Jain

TL;DR: The final model, CheXbert, is able to outperform the previous best rules-based labeler with statistical significance, setting a new SOTA for report labeling on one of the largest datasets of chest x-rays.

TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.

TL;DR: RadGraph, a dataset of entities and relations in full-text chest X-ray radiology reports based on a novel information extraction schema, is presented, which can facilitate a wide range of research in medical natural language processing, as well as computer vision and multi-modal learning when linked to chest radiographs.

TL;DR: The authors proposed a BERT-based approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations, and demonstrated superior performance of a biomedically pretrained BERT model first trained on annotations of a rulebased labeler and then finetuned on a small set of expert annotation augmented with automated backtranslation.

TL;DR: In this article, the authors used a BERT model to directly map from a radiology report to the image labels, with a supervisory signal determined by a computer vision model trained to detect medical conditions from chest X-ray images.