MIT Clinical and Applied Machine Learning

Victor Butoi @ion_barrel · Sep 24

X-rays are everywhere in medicine, but extracting general, quantitative anatomical information from them remains remarkably difficult.

Announcing FleXray: an open-source, open-weight model for zero-shot anatomical segmentation across the human body—from different regions, viewing angles, and acquisition settings.

FleXray learns from large-scale synthetic supervision generated from 3D anatomy, then transfers directly to real clinical X-rays.

Try it now on your own X-rays in our in-browser demo!

🧵 1/N — project page, demo, weights, code & paper below ↓

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Hallee Wong @HalleeWong · Sep 27, 2024

Presenting ScribblePrompt: a lightweight interactive segmentation tool that enables users to perform new biomedical image segmentation tasks using a few bounding boxes, clicks and scribbles, to appear at #ECCV2024 🎉

Work w/ @MarianneRakic, John Guttag and @AdrianDalca

1/

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Marianne Rakic @MarianneRakic · Apr 22, 2024

So excited that Tyche is a highlight at @CVPR  🥳

Tyche is a stochastic strategy for in-context medical image segmentation, to both generalize to new tasks and capture uncertainty.

Work with @AdrianDalca @HalleeWong @jjgort John Guttag and @CiminiLab

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Victor Butoi @ion_barrel · Aug 29, 2023

Presenting UniverSeg: an in-context learning model for medical image segmentation to appear at #ICCV2023 🎉! (w/ @jjgort, @mertrory, @AdrianDalca, and others)

@MIT_CSAIL, @MIT, @MGHMartinos

🧵1/N (project-page, demo, and paper links 🔗at the end)

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