Only 3% of Black students learn computer science in high school or beyond. Please watch and share this video. Inspire a student. Together we can change the face of computer science. #BlackVoicesForCS
In image analysis, we often assume subjects may be easily identified as subgroup members. But what if this information is only partially encoded in the data?
Our MICCAI 2023 paper investigates "The Role of Subgroup Separability in Group-Fair Medical Image Classification". (1/6)
Network neuroscience is awesome ! 🧠But there are soo many ways to go from fMRI data to a connectome that you can analyse... 😵
Are they all just as good? Not at all!
We compare 576 unique pipelines on 3 datasets across 5 different criteria, so you don't have to!
Join us through the fascinating world of brain connectivity at @CD_MRI! 🧠🔗🔬 Hear from our keynotes: Dr. Chun-Hung Yeh 🇹🇼, Dr. Demian Wassermann 🇫🇷, and Dr. Jennifer A. McNab 🇺🇸Don't miss this unique opportunity to explore the latest advancements! #MICCAI2023@MICCAI_Society
Very glad to share our paper was accepted at the Journal of Neurotrauma. Check out the paper published in collaboration with @CenterTBI @SWinzeck @jansijbers @vfjn2 @Menon_Cambridge https://t.co/hkFZb0Audq
We are at an inflection point for the use of AI in health care. Microsoft Research’s Health Futures organization shares its work to make health care more data-driven, predictive, and precise: https://t.co/3904QZmsxR
Editor pick from March issue
@GlockerBen & coll
shed some light on how dataset biases manifest in predictive models
by exploring methodology for subgroup analysis in image-based disease detection models.
Read https://t.co/24UbBYmPna
#bias#disparities#fairness
In semantic segmentation, background samples provide key contextual information for segmenting regions of interest (ROIs). Our lastest @IEEE_TMI paper systematically investigates the effect of context labels on model learning, especially with small ROIs.
https://t.co/Ztrsy6jsrd
Do you provide #CriticalCare to patients with #TBI ?
We found that serum biomarkers could reduce the need for #MRI by 20-30% whilst being cost-saving
Find out more in our latest paper https://t.co/wjZDCvNIrc
@Crit_Care
Thanks @CenterTBI @Menon_Cambridge@vfjn2 & co-authors
ARE YOU POOLING multi-site neuroimaging data? Yes, you! ;)
Do you the differences between ComBat🤺, Hierarchical Bayes regression, normative methods, + CycleGANs🚴?
Johanna Bayer's guided tour explains when, why, + how to use these methods https://t.co/Ja7Ppfllfo
Hello neuroimagers! @CenterTBI @enigmabrains@ADNI3study
Do you use multi-site #MRI data & wonder what the best ComBat harmonization method is for your data?
Look @ our new paper https://t.co/e7TvOBgH2Q
Building on great work of @chrisdav66@takishinohara et al
Thx @vfjn2
Many MNI spaces! Also many parcellations/templates in many different spaces. This is one of my favorite resources from Lawrence et al. and JHU team: https://t.co/vXaVFnJYb7. Huge number of parcellations all in MNI152NLin6 space.
Want to run connectomics analyses🧐even in absence of DWI data? We provide a new multi-scale probabilistic atlas of the human connectome and user-friendly code to match it to your data🧠led by Yasser Aléman-Gómez @MeriBach@maxdescoteaux@pahagman et al. https://t.co/400FzCdobg