Julius Hense

✨ Our xMIL paper has been accepted at #NeurIPS2024 ✨ arXiv preprint: arxiv.org/abs/2406.04280

Original Article by @jdppel et al.: AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics nejm.ai/3YwvUZz #ArtificialIntelligence

A deep anomaly detection approach for histopathology shows high detection performance for a broad range of diseases (including all cancers) within the long diagnostic tail in gastrointestinal biopsies. Read the full article by @jdppel et al.: nejm.ai/3YwvUZz

Big congrats to our shooting star Jonah Kömen, who produced these results in his Bachelor thesis (‼), to my amazing co-advisors Hannah Marienwald and @jdppel, and to everyone involved at @bifoldberlin and @ml_tuberlin.

❓ Do histopathological foundation models eliminate batch effects? ❓ The surprisingly clear answer is: they do not! Find out more in our new paper that we will present at the AIM-FM Workshop at @NeurIPSConf. Link: arxiv.org/abs/2411.05489

If two models are more similar to each other than a third on ImageNet, will this hold for medical/ satellite images? Our preprint analyzes how vision model similarities generalize across datasets, the factors that influence them, and their link to downstream task behavior. 🧵1/7

🎉 Thrilled to announce that our paper MambaLRP has been accepted at #NeurIPS2024! Explain your Mamba models in a faithful, efficient, and effective way! 🐍 Huge thanks to @EberleOliver, Grégoire Montavon, and Klaus-Robert Müller @bifoldberlin, @ml_tuberlin , @GoogleDeepMind,

Our work on historical insights at scale using machine learning is now out in @ScienceAdvances! Very proud of this team effort, bridging disciplines and institutions—@MPIWG @TUBerlin @bifoldberlin @ml_tuberlin 📜science.org/doi/10.1126/sc…

🧵I am excited to share that our paper: AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics has now been published in @NEJM_AI . ai.nejm.org/doi/full/10.10…

Massive congrats to the incredibly talented first authors @jdppel and @n_prenissl, as well as to the whole team: Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, Maximilian Alber, @lukasruff, Klaus-Robert Müller, @FKlauschen.

✨ Our paper on Anomaly Detection for Histopathology has been published in @NEJM_AI ✨ Our paper shows how to build AI that detects ANY infrequent disease without training data collection. Link: ai.nejm.org/doi/full/10.10…

🔊 Proud that our lab has four #NeurIPS2024 publications this year! 🎉 Check them out 👇 #MachineLearning #Artificalintelligence

🚨 PhD Opportunity! 🚨 Apply your skills in #DeepLearning & #ComputerVision to cancer research! Join us to study B cell dynamics & ECM in melanoma, using cutting-edge imaging & multiomics like #SpatialTranscriptomics. Apply: colcc.ac.uk/2025-phd-proje… @CRUKCOLcentre @QMBCI

Big thanks to my collaborators and mentors, first and foremost @IdajiMina, as well as @EberleOliver, @thomschnake, @jdppel, @lciernik, Oliver Buchstab, @am0ck, Frederick Klauschen, Klaus-Robert Müller, and all the supporters from @bifoldberlin and @ml_tuberlin.

✨ Our xMIL paper has been accepted at #NeurIPS2024 ✨ arXiv preprint: arxiv.org/abs/2406.04280