@frazierhuo.bsky.social
Reposted
🎉 Excited to present our #ICLR2025 work—leveraging future medical outcomes to improve pretraining for prognostic vision models.
🖼️ "Time-to-Event Pretraining for 3D Medical Imaging"
👉 Hall 3+2B #23
📍 Sat 26 Apr, 10 AM–12:30 PM
🔗 iclr.cc/virtual/2025...
🖼️ "Time-to-Event Pretraining for 3D Medical Imaging"
👉 Hall 3+2B #23
📍 Sat 26 Apr, 10 AM–12:30 PM
🔗 iclr.cc/virtual/2025...
ICLR Poster Time-to-Event Pretraining for 3D Medical ImagingICLR 2025
iclr.cc
April 23, 2025 at 9:00 PM
🎉 Excited to present our #ICLR2025 work—leveraging future medical outcomes to improve pretraining for prognostic vision models.
🖼️ "Time-to-Event Pretraining for 3D Medical Imaging"
👉 Hall 3+2B #23
📍 Sat 26 Apr, 10 AM–12:30 PM
🔗 iclr.cc/virtual/2025...
🖼️ "Time-to-Event Pretraining for 3D Medical Imaging"
👉 Hall 3+2B #23
📍 Sat 26 Apr, 10 AM–12:30 PM
🔗 iclr.cc/virtual/2025...
🚀 Public releases 𝘁𝗵𝗿𝗲𝗲 𝗱𝗲-𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝗹𝗼𝗻𝗴𝗶𝘁𝘂𝗱𝗶𝗻𝗮𝗹 𝗘𝗛𝗥 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 (EHRSHOT, INSPECT, MedAlign) with 25,991 patients & 295M clinical events (1997-2023)—now freely available!
These datasets support 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝘁𝗶𝗺𝗲-𝘁𝗼-𝗲𝘃𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, and 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝘁𝗮𝘀𝗸𝘀.
These datasets support 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝘁𝗶𝗺𝗲-𝘁𝗼-𝗲𝘃𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, and 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝘁𝗮𝘀𝗸𝘀.
[1/4] 🎉 We're thrilled to announce the general release of three de-identified, longitudinal EHR datasets from Stanford Medicine—now freely available for non-commercial research use worldwide! 🚀
Learn more on our HAI blog:
hai.stanford.edu/news/advanci...
Learn more on our HAI blog:
hai.stanford.edu/news/advanci...
Advancing Responsible Healthcare AI with Longitudinal EHR Datasets
Current evaluations of AI models in healthcare rely on limited datasets like MIMIC, lacking complete patient trajectories. New benchmark datasets offer an alternative.
hai.stanford.edu
February 13, 2025 at 9:07 PM
🚀 Public releases 𝘁𝗵𝗿𝗲𝗲 𝗱𝗲-𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝗹𝗼𝗻𝗴𝗶𝘁𝘂𝗱𝗶𝗻𝗮𝗹 𝗘𝗛𝗥 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 (EHRSHOT, INSPECT, MedAlign) with 25,991 patients & 295M clinical events (1997-2023)—now freely available!
These datasets support 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝘁𝗶𝗺𝗲-𝘁𝗼-𝗲𝘃𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, and 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝘁𝗮𝘀𝗸𝘀.
These datasets support 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝘁𝗶𝗺𝗲-𝘁𝗼-𝗲𝘃𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴, and 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝘁𝗮𝘀𝗸𝘀.
[1/4] Excited to share that our paper 𝘛𝘪𝘮𝘦-𝘵𝘰-𝘌𝘷𝘦𝘯𝘵 𝘗𝘳𝘦𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 3𝘋 𝘔𝘦𝘥𝘪𝘤𝘢𝘭 𝘐𝘮𝘢𝘨𝘪𝘯𝘨 is accepted at ICLR 2025! 🚀
We introduce 𝗧𝗧𝗘 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, using EHR-linked imaging to improve AI-driven prognosis—essential for assessing disease progression.
🔗 Paper: arxiv.org/abs/2411.09361
We introduce 𝗧𝗧𝗘 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, using EHR-linked imaging to improve AI-driven prognosis—essential for assessing disease progression.
🔗 Paper: arxiv.org/abs/2411.09361
Time-to-Event Pretraining for 3D Medical Imaging
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disea...
arxiv.org
February 2, 2025 at 6:10 AM
[1/4] Excited to share that our paper 𝘛𝘪𝘮𝘦-𝘵𝘰-𝘌𝘷𝘦𝘯𝘵 𝘗𝘳𝘦𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 3𝘋 𝘔𝘦𝘥𝘪𝘤𝘢𝘭 𝘐𝘮𝘢𝘨𝘪𝘯𝘨 is accepted at ICLR 2025! 🚀
We introduce 𝗧𝗧𝗘 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, using EHR-linked imaging to improve AI-driven prognosis—essential for assessing disease progression.
🔗 Paper: arxiv.org/abs/2411.09361
We introduce 𝗧𝗧𝗘 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, using EHR-linked imaging to improve AI-driven prognosis—essential for assessing disease progression.
🔗 Paper: arxiv.org/abs/2411.09361