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Poster #280!

"Artificial intelligence can help doctors spot disease, but it's not taking over medicine" AI could take medical imaging to the next level  sciencenews.org/article/ai-med…

Congrats to Adrit Rao and mentor Oliver Aalami on their open source aorta diameter measurement tool. This is an important step towards standardizing AAA measurement and longitudinal monitoring.

Congrats Arash 🚀 Huge contributions by Adrit Rao. Also, Louis Blankemeier, Akshay Chaudhari, Pauline Berens, and Andrea Fisher The open-source A4 pipeline to automatically provide max AAA diameter from Abdominal/Pelvic CT scans is meant to be a starting point for any project that

My takeaways from the recent "An Image is Worth More Than 16x16 Patches" arxiv.org/abs/2406.09415 - Given a fixed compute budget, increasing the image resolution is better than decreasing the patch size. Only when the input image resolution is maximized, should we decrease the

In 2023, the US paid $658 billion in interest on the national debt. Excluding Federal Reserve Banks, the plurality of that interest was paid to mutual funds. Individual income tax revenue in 2023 was $2.2 trillion. So, an amount equivalent to 30% of the money collected from

🎉📝 Immensely honored to share that our #RoentGen model for creating synthetic chest X-rays is now published in Nature Biomedical Engineering! 📝rdcu.be/dR0sE 1/🧵Pierre Chambon Curt Langlotz Tanishq Mathew Abraham, Ph.D. Akshay Chaudhari Stanford AIMI #AI #Radiology #GenAI

Our RoentGen paper is now out in Nature Biomedical Engineering with more exps than before! There's lots of excitement/debate in best practices for synthetic data in training next-gen foundation models. We hope our work in medical imaging can add to this dialogue! Congrats Christian Bluethgen+Stanford Radiology team!

Want to understand why a computed tomography classifier made a prediction? The code just went online for generating counterfactual explanations for 2D/3D CT classifiers! Here is an explanation for Plural Effusion: #radiology #radiologyai Stanford AIMI github.com/ieee8023/ct-co…

So proud of the release of the GREEN metric. See what you can do when you merge medical AI research and open-sourceness. 26,000 downloads on 🤗 Hugging Face and counting. 🟥 EMNLP proceedings: aclanthology.org/2024.findings-… 🤗 Dataset: huggingface.co/datasets/Stanf… 🤗 Models:

The most powerful agent, interpreting X-rays is opensource🤗

Incredible work led by Zhihong Chen and Maya Varma: ✅ Transparent, open-source benchmarks ✅ Suite of open-source models ✅ Open-source instruction tuned model trained on 35 tasks ✅ Open-source code for curating CheXinstruct dataset Most notably, this research demonstrates

🎉 Excited to share that our latest research, 𝘛𝘪𝘮𝘦-𝘵𝘰-𝘌𝘷𝘦𝘯𝘵 𝘗𝘳𝘦𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 3𝘋 𝘔𝘦𝘥𝘪𝘤𝘢𝘭 𝘐𝘮𝘢𝘨𝘪𝘯𝘨, has been accepted at 𝗜𝗖𝗟𝗥 2025! 🚀 🔍 𝗜𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗜𝗺𝗮𝗴𝗲 𝗣𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗶𝗺𝗲-𝘁𝗼-𝗘𝘃𝗲𝗻𝘁

🧵 What if AI could learn from millions of unlabeled radiology images and reports—and then flexibly adapt to new clinical tasks? In a new comprehensive review in Radiology, we dive into how foundation models (FMs) are set to revolutionize radiology! @AIMI_Stanford (1/6) 👇