RT @lciernik: 🎉 Update: This work got accepted to #icml2025!!. Huge thanks to my amazing co-authors @LorenzLinhardt, Marco Morik, @jdppel,….

RT @MayoClinic: Mayo Clinic announced the formation of Mayo Clinic Digital Pathology, designed on a platform architecture to boldly unlock….

RT @NEJM_AI: Original Article by @jdppel et al.: AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics .

RT @NEJM_AI: A deep anomaly detection approach for histopathology shows high detection performance for a broad range of diseases (including….

RT @lciernik: If two models are more similar to each other than a third on ImageNet, will this hold for medical/ satellite images? Our prep….

RT @EberleOliver: Our work on historical insights at scale using machine learning is now out in @ScienceAdvances! Very proud of this team e….

@bifoldberlin @ml_tuberlin @TUBerlin @ChariteBerlin @LMU_Muenchen @rptu_kl_ld @AIgnostics.

🙏 Thanks to my co-first author @n_prenissl , stellar supervision by @lukasruff , Klaus-Robert Müller, @FKlauschen and amazing contributions by @hense96, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, and Maximilian Alber.

✨On two large clinical cohorts of gastrointestinal biopsies, we demonstrate that our anomaly detection approach can reliably detect a broad spectrum of anomalous pathologies, including rare cancer types.

🛠️We compare two promising approaches. (1) using representation distances from SSL/foundation models and (2) contrasting normal examples with images from other tissue types which helps to learn a tight decision boundary around the normal data (outlier exposure).

💡Detecting rare diseases is challenging as often not enough training data is available to train a supervised model. We circumvent this by proposing an anomaly detection approach which learns the characteristics of healthy tissue and common findings and then detects any deviation.

🧵I am excited to share that our paper: AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics has now been published in @NEJM_AI .

RT @FriedaBorn: Why do some memories fade in seconds, while others stay with us for life? Working Memory (WM) holds info for just moments,….

RT @AIgnostics: The #aignostics team is heading to #ASCO2024!. Interested in hearing about how we’re transforming precision medicine with o….

RT @ml_tuberlin: 🚨 We have 2 open #PhD positions! . Come join us @TUBerlin. We offer cutting edge #research ranging from #XAI over #Probab….

RT @bifoldberlin: Congrats to the team. The paper “Improving neural network representations using human similarity judgments” will be prese….

RT @matthiasboehm7: 🎇 We have another opening for a PhD student position in the @bifoldberlin agility project 'LungCAIRE' with Charité on m….

We built a new website for our lab🧑‍💻 Check out researchers, publications, courses and software from our group!.

RT @EberleOliver: Come pursue your PhD and work with us @TUBerlin @bifoldberlin with @MPIWG @M_Valleriani @ml_tuberlin . Full-time Resear….