🚀 Excited to introduce: "Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations", wich will be presented as an oral at the #RROW Workshop at #BMVC2024 🏴󠁧󠁢󠁳󠁣󠁴󠁿. 📝: arxiv.org/abs/2409.12276 🐙: github.com/sdoerrich97/unora… 👥: @francescodis_, @LedigChr

🏃‍♂️ MICCAI Student Board Morning run was a big success! Thank you everyone for attending! And thank you @consti_ulrich for organising! 🏃‍♀️ #MICCAI #MICCAI2024

📣 Our paper "Privacy-preserving datasets by capturing feature distributions with Conditional VAEs," has been accepted at #BMVC2024 🏴󠁧󠁢󠁳󠁣󠁴󠁿 A huge thank you to all co-authors: David Tafler, @DoerrichS, and @LedigChr. Paper: arxiv.org/abs/2408.00639

Another One!📢 Excited to share that our paper "Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization" is accepted at #MICCAI2024! Want to know more? 📝: arxiv.org/abs/2407.02900 🐙: github.com/sdoerrich97/vits-… @MICCAI_Society @xAILabBamberg

Best of luck for what comes next @ilyasut. Looking forward to knowing more about it. 🚀 In the meanwhile, come give a talk to the @M2lSchool! 🧑‍🏫👀 Milan in September is fabolous 🌞🍝🍷

📝Paper Alert!📝 Check out our latest research: "Rethinking Model Prototyping through the MedMNIST Dataset Collection" - the first baseline evaluation for the MedMNIST database. 📝: arxiv.org/abs/2404.15786 🐙: github.com/sdoerrich97/rethi… 💾: zenodo.org/records/10519652 @xAILabBamberg

Big shoutout to Tobias for securing third place at the @_DMEA Newcomer Award!🥉 Please join us at the xAI Lab Bamberg in celebrating this remarkable achievement!👏 Learn more: uni-bamberg.de/en/xai/news/a… Explore our research at xAI Lab Bamberg: uni-bamberg.de/en/ai/chair-o… @UniWiai

🌟 Celebrating the Next! 🌟 Tobias, one of my exceptional students, is a finalist for the prestigous "Newcomer Award" at @_DMEA 2024! Please join me in celebrating this outstanding achievement by voting for his contribution:👍 youtube.com/watch?v=x45XKq3Q… @xAILab @UniWiai

GPT-4 with simple engineering can predict the future around as well as crowds: arxiv.org/abs/2402.18563 On hard questions, it can do better than crowds. If these systems become extremely good at seeing the future, they could serve as an objective, accurate third-party. This would help us better anticipate the longterm consequences of our actions and make more prudent decisions. "The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom." - Asimov I didn't write this paper, but we called for AI forecasting research in Unsolved Problems in ML Safety some years back (arxiv.org/abs/2109.13916), and concretized as a research avenue a year later in Forecasting Future World Events with Neural Networks (arxiv.org/abs/2206.15474). Hopefully AI companies will add this feature as the election season begins.

There is a saying in #radiology, “One view is no view.” When generating radiology reports for most exam types, one image is no image. #GPT4V is impressive, but you must ask the right questions & be careful not to overhype #AI, esp. in #healthcare. #GenAI #RadTwitter #MedTwitter

🎓Interested in pursuing fully-funded PhD or PostDoc research in (medical) image computing or explainable machine learning in general? 🚨We are currently hiring in our xAI-Lab @uni_bamberg_of Get in touch and we can chat at #MICCAI2023 @MICCAI_Society Details below.

If you're around, feel free to drop by our poster presentation tomorrow at #MICCAI23. 🗓️ Date: Sunday, 8th of October 📍 Room: Poster Hall, Ground Level Exhibition B-C 🕒 Time: 1:30 - 2:15 PM 🖼️ Poster: P-7 Looking forward to sharing insights and discussions! See you there!🚀

🎉Excited to share our paper at #MICCAI23 #MLMI: "unORANIC", an unsupervised orthogonalization of anatomy and image characteristics. 👀No need for domain knowledge 👊Improved robustness against corruption Prep.: arxiv.org/abs/2308.15507 Source: github.com/sdoerrich97/unORA… @xAILab

A bunch of people have requested the slides for my "Scholars & Big Models" CVPR workshop talk. I didn't have a script, but I wrote a rough version of what I probably said at the bottom of each slide. Feedback is welcome! jonbarron.info/data/cvpr2023…

Check out our latest walkthrough published in @TDataScience on generating medical images from text with MONAI generative models! towardsdatascience.com/gener…

@fracesomotagna presenting his poster on “Scalable Causal Discovery with Score Matching” at #CLeaR2023 arxiv.org/abs/2304.03382

Just tested @MetaAI 's segmentation model, SAM, on medical images covering a range of surgical images (Image 1) and a diverse range of medical imaging data including X-rays, MRIs, and CT scans (Image 2) and some unlabeled medical data (Image 3). My finding & submission👇

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models Presents a comprehensive survey of ChatGPT and GPT-4 and their prospective applications across diverse domains. arxiv.org/abs/2304.01852