📢New preprint and data alert! 1/ EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data Preprint: https://t.co/tbOfu8Wt0E Dataset: https://t.co/22NHqWdVLA More in the thread 🧵

Work done with the super awesome —Jacques Delfrate and @RobertAvramMD at the https://t.co/AD7Yr6e21s @ Montreal Heart Institute (@ICMtl)

Presenting our work on ECG signal tokenization and automated report generation, interpretation at the #NeurIPS2025 Foundation Models for the Brain and Body workshop, tomorrow! If you're interested in Multimodal AI and Healthcare, let's chat! https://t.co/tb9Ym4n7kA

🧠 Working on a radiology or cardiology AI model? Bring YOUR model to PACS-AI. We’re building an open-source platform to run CV models directly in clinical imaging workflows. 🚀 Featured in CIFAR Reach → https://t.co/yipXvXhYA5 📬 DM me for Slack access 🔓 Open release this

Always a computer science nerd at heart — grateful to put it to good use helping people and making a real difference in medicine. Thank you to the Fondation de l’Institut de Cardiologie de Montréal @ICMtl for the feature and support. https://t.co/XqHKV8b93c

New preprint! 🧠🤖 How do we build neural decoders that are: ⚡️ fast enough for real-time use 🎯 accurate across diverse tasks 🌍 generalizable to new sessions, subjects, and species? We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes! 🧵1/7

Every frontier AI system should be grounded in a core commitment: to protect human joy and endeavour. Today, we launch @LawZero_, a nonprofit dedicated to advancing safe-by-design AI. https://t.co/6VJecvaXYT

🚨 Preprint Alert 🚀 📄 seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models https://t.co/vJaFyoQZvV Can we simultaneously learn both transformation-invariant and transformation-equivariant representations with self-supervised learning (SSL)?

EXCITED To share our publication on the clinical Deployment of Real-Time LVEF estimation during coronary angiogram in @NEJM_AI ! CathEF, a deep learning model integrated into PACS-AI ( https://t.co/NCY1a0x8RV), estimates left ventricular ejection fraction (LVEF) in real time from

Introducing nanoAhaMoment: Karpathy-style, single file RL for LLM library (<700 lines) - super hackable - no TRL / Verl, no abstraction💆‍♂️ - Single GPU, full param tuning, 3B LLM - Efficient (R1-zero countdown < 10h) comes with a from-scratch, fully spelled out YT video [1/n]

𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐨𝐥𝐨𝐠𝐲 paper is out! 🔥🐋 We study the reasoning chains of DeepSeek-R1 across a variety of tasks and settings and find several surprising and interesting phenomena! Incredible effort by the entire team! 🌐: https://t.co/CDlFHD28xQ

🚀 Super excited to announce UI-Vision: the largest and most diverse desktop GUI benchmark for evaluating agents in real-world desktop GUIs in offline settings. 📄 Paper: https://t.co/qJprMkzzH3 🌐 Website: https://t.co/N4hCpcd8dK 🧵 Key takeaways 👇

Presenting ✨ 𝐂𝐇𝐀𝐒𝐄: 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐢𝐧𝐠 𝐬𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 ✨ Work w/ fantastic advisors @DBahdanau and @sivareddyg Thread 🧵:

Just got this server set up in my lab, what should we test it on? TY @Exxactcorp

8/ The method is open-source and available as part of the Spinal Cord Toolbox (SCT)🚀 Have questions or feedback? Let us know! We’d love to hear how EPISeg can support your research.

7/ This work wouldn’t have been possible without the amazing contributions from multi-site collaborators, open-source community, @PolyNeuro , @Mila_Quebec and @StanfordPain . We hope EPISeg helps research paving the way for new insights into spinal cord function and dysfunction.

6/ What sets EPISeg apart? • Trained via active learning, improving robustness over multiple iterations. • Works across different scanner protocols and resolutions. • Handles clinical cases, including data from patients with myelopathy and fibromyalgia.

5/ To build EPISeg, we created an open-access dataset of SC fMRI along with the segmentations from 15 sites, covering 406 participants with varying scanner setups and conditions.

4/ Our solution: EPISeg uses a deep learning model trained on diverse, multi-center dataset of gradient-echo EPI images to perform fully automated SC segmentation. It’s fast, accurate, and robust to: • Low-resolution images • Distortions • Signal drop-outs • Motion artifacts

3/ Until now, spinal cord (SC) segmentation on EPI data required time-consuming manual corrections prone to user bias and errors.

2/ Spinal cord fMRI is critical for studying sensation, movement, and autonomic function. However, preprocessing SC fMRI data like segmenting the spinal cord is challenging due to low spatial resolution, susceptibility artifacts, motion and ghosting artifacts and poor SC contrast