🎉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/un… @xAILab

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!🚀

🚀Exciting news!🚀 Our paper on "Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification" has been accepted at #ISBI2024! Preprint📝: arxiv.org/abs/2402.12500 Code🐙: github.com/TobArc/privacy… @xAILab

🌟 Celebrating the Next! 🌟 Tobias, one of my exceptional students, is a finalist for the prestigous "Newcomer Award" at DMEA - Connecting Digital Health 2024! Please join me in celebrating this outstanding achievement by voting for his contribution:👍 youtube.com/watch?v=x45XKq… @xAILab Fakultät WIAI - Uni Bamberg

Big shoutout to Tobias for securing third place at the DMEA - Connecting Digital Health Newcomer Award!🥉 Please join us at the xAI Lab Bamberg in celebrating this remarkable achievement!👏 Learn more: uni-bamberg.de/en/xai/news/ar… Explore our research at xAI Lab Bamberg: uni-bamberg.de/en/ai/chair-of… Fakultät WIAI - Uni Bamberg

📝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/re… 💾: zenodo.org/records/105196… @xAILabBamberg

If you're at #ISBI2024 in #Athens right now, feel free to drop by our poster presentation tomorrow. 🗓️Date: Tuesday, 28th of May 2024 📍 Room: Banqueting Hall Foyer 🕒Time: 10:00 - 11:30 AM 🖼️Poster: #980 See you there and if you want to know more, check out the post below!🚀

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/vi… MICCAI @xAILabBamberg

🌟 xAILAB Bamberg goes MICCAI - Part II 🌟 Another work from our group was accepted for #MICCAI2024! This work led by Francesco will be presented at the Workshop on Advancing Data Solutions in Medical Imaging AI #ADSMI. 📝 arxiv.org/pdf/2406.17536 🐙 github.com/francescodisal…

🎺Excited to share yet another work of our #xAILabBamberg! This work, led by Francesco, is for all of you #ComputerVision folks out there interested in #privacy preserving and will be featured at this year's #BMVC conference in Glasgow, UK! 🏴󠁧󠁢󠁳󠁣󠁴󠁿 📝: arxiv.org/abs/2408.00639

🚀 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/un… 👥: Francesco, Christian Ledig

If you're interested in histopathology and are attending #MICCAI2024 in 🇲🇦, feel free to drop by my poster tomorrow. 🗓️ Tuesday, 8th of October 2024 🕒 10:30 - 11:30 AM 🖼️ Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization [T-AM-233]

If you are at #BMVC2024 in Glasgow 🏴󠁧󠁢󠁳󠁣󠁴󠁿 and are interested in robustness for medical image diagnosis, feel free to drop by my oral tomorrow. 🗓️ Wednesday, October 27, 2024 📍 M3 🕒 11 AM 🖼️ Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations

🚀 Looking for a comprehensive benchmark for the MedMNIST+ database? Check out our latest paper, "Rethinking model prototyping through the MedMNIST+ dataset collection" now published open-access in Scientific Reports 📝 nature.com/articles/s4159… 🐙 github.com/sdoerrich97/re…

🚀 Looking for a novel method to enhance classification robustness under real-world label noise? Check out this work "An Embedding is Worth a Thousand Noisy Labels" led by Francesco which was published in Transactions on Machine Learning Research. 📝 openreview.net/forum?id=X3gSv… 🐙 github.com/francescodisal…