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While it still feels (very) surreal... I defended & turned in my thesis last week!

I'm looking for a postdoc to focus on machine learning and health methods that target improved robustness and fairness at MIT in Fall 2023. DM me if you have a good candidate, including yourself!

"UniverSeg: Universal Medical Image Segmentation" What if we could train a single neural net to highlight important structures in any medical image given just a few examples? [1/13]

I'm presenting Kaleidoscope, a system for user-driven, context-specific, and semantically-meaningful ML model evaluation at #CHI2023! dl.acm.org/doi/10.1145/35…

1/ Patient race in health datasets is often reported coarsely: for example, both Indian and Chinese patients are categorized as “Asian”. Does the coarse coding of race hide disparities in clinical machine learning performance? In our new working paper (!), we find that it does.

New working paper! We study the impact of coarse race categories - think "Asian", "Black", "White" - on the study of predictive disparities in health.

In Vancouver for #CVPR2023 ! Excited to chat about multimodal generative models, especially in the context of creativity or health - please reach out if these are of interest to you.

Steffen Czolbe is presenting at #CVPR2023 today (TUE-PM-200 in person) Neuralizer: General Neuroimage Analysis without Re-Training code: github.com/SteffenCzolbe/… video: youtu.be/_wgCESDwjZI FreeSurfer MGH Martinos Center MIT Clinical and Applied Machine Learning MIT CSAIL MIT EECS #CVPR2025

In New York today to present a paper with Raj Movva on the impact of coarse race variables on the study of disparities in clinical risk score performance!

Presenting UniverSeg: an in-context learning model for medical image segmentation to appear at #ICCV2023 🎉! (w/ Jose Javier Gonzalez, Mert R. Sabuncu 🤖🩻⚕️, Adrian Dalca, and others) MIT CSAIL, Massachusetts Institute of Technology (MIT), MGH Martinos Center 🧵1/N (project-page, demo, and paper links 🔗at the end)

Check out UniverSeg! Recent #ICCV2023 work from our lab led by co-first authors @VictorButoi and Jose Javier Gonzalez. x.com/VictorButoi/st…

New in-context learning work from our lab just dropped! Checkout out Tyche, to appear at #CVPR2024 as a ✨highlight✨, led by Marianne Rakic. x.com/MarianneRakic/…

I'll be at the ICLR DMLR and PMLDS workshops presenting ongoing work with Divya Shanmugam (+ a wonderful team including Manish Raghavan, MIT Clinical and Applied Machine Learning, Bonnie Berger Lab, Emma Pierson)! Details in thread below 🧵 [1/4]

🏆Our Bench-to-Bedside Award goes to: "ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image" by Hallee E. Wong, Marianne Rakic, John Guttag, Adrian V Dalca Congratulations! 🎉

To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. 🩻 When trained to understand the boundaries of biological structures, AI systems can delineate regions of interest for biomedical workers. MIT CSAIL, MGH, and

.MIT CSAIL PhD student Marianne Rakic's most recent project, Tyche, is a medical image segmentation model that aims at generalizing new tasks & capturing uncertainty in the medical image. Learn more about Marianne and her recent projects: bit.ly/4d6X39t

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/ Marianne Rakic, John Guttag and Adrian Dalca 1/

.MIT CSAIL PhD student Marianne Rakic's most recent project, Tyche, is a medical image segmentation model that aims at generalizing new tasks & capturing uncertainty in the medical image. Learn more about Marianne and her recent projects: bit.ly/4d6X39t

Checkout Hallee Wong 's awesome work on building a general-purpose interactive segmenter, appearing now at #ECCV2024! x.com/HalleeWong/sta…

Very excited to present ScribblePrompt at #ECCV2024 this afternoon (Tue Oct 1, 16:30-18:30 CEST)! Stop by poster # 70 if you are around!