MIT Clinical and Applied Machine Learning
@mit_caml
Clinical and Applied Machine Learning Group @MIT_CSAIL. Led by Prof John Guttag, we focus on clinically inspired machine learning with real-world relevance.
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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!
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/
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/
Checkout @HalleeWong 's awesome work on building a general-purpose interactive segmenter, appearing now at #ECCV2024! x.com/HalleeWong/sta…
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/
.@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
.@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
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…
🏆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! 🎉
New in-context learning work from our lab just dropped! Checkout out Tyche, to appear at #CVPR2024 as a ✨highlight✨, led by @MarianneRakic. x.com/MarianneRakic/…
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
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)
Check out UniverSeg! Recent #ICCV2023 work from our lab led by co-first authors @VictorButoi and @jjgort.
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)
In New York today to present a paper with @rajivmovva on the impact of coarse race variables on the study of disparities in clinical risk score performance!
@SCzolbe 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 @FreeSurferMRI @MGHMartinos @mit_caml @MIT_CSAIL @MITEECS @CVPR
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.
New working paper! We study the impact of coarse race categories - think "Asian", "Black", "White" - on the study of predictive disparities in health.
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.
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.
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…
"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]
![davisblalock's tweet image. "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]](https://pbs.twimg.com/media/FuTYS9QaQAEItlQ.jpg)
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!
While it still feels (very) surreal... I defended & turned in my thesis last week!
So proud of Harini Suresh (@harini824) for (successfully!) defending a tour de force of a thesis on context and participation in machine learning. Pictured here: CAML reciprocates the immeasurable support she's given the lab over the course of her PhD 🤩🎉
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