Ekin Tiu

[Nature Biomedical Engineering @natBME]

A self-supervised model trained on chest X-ray images that lack explicit annotations performs pathology-classification tasks with accuracies comparable to those of radiologists.

https://t.co/7msqvEmTiy

[Isaac Kohane @zakkohane]

Self-supervised (no expensive detailed annotation) classification of chest X rays pathology "look Ma, no annotations" @natBME https://t.co/tV1E4Mm3ZR in a collaboration across @Stanford @HarvardDBMI w @pranavrajpurkar @curtlanglotz @AndrewYNg and Ekin Tiu

[Pranav Rajpurkar @pranavrajpurkar]

Great coverage of our recent #CheXzero publication for self-supervised learning using radiology images and reports. Coverage by @EkaterinaPeshev @harvardmed

https://t.co/EW9bvftOMx

[Pranav Rajpurkar @pranavrajpurkar]

Today, we're publishing CheXzero in @natBME 🚀

We show we can detect diseases from chest X-ray images at the level of radiologists *without labeling for individual diseases*.

How do we do it? By teaching the model to read the radiology report.
https://t.co/lakxNzWQH3