PhD Student at @ml_tuberlin, @bifoldberlin and Data Scientist at @aignostics | Training large neural nets for computational pathology.
Berlin, Germany
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Jonas Dippel retweeted
🎉 Update: This work got accepted to #icml2025!!
Huge thanks to my amazing co-authors @LorenzLinhardt, Marco Morik, @jdppel, @skornblith, and @lukas_mut for their great work and to all collaborators! 🙏
📄 Paper: arxiv.org/abs/2411.05561
💻 Code: github.com/lciernik/similari…
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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
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Jonas Dippel retweeted
Mayo Clinic announced the formation of Mayo Clinic Digital Pathology, designed on a platform architecture to boldly unlock the power of its extensive archive of digital slides to revolutionize pathology and accelerate medical breakthroughs.
Learn more: mayocl.in/4aeawwi
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Jonas Dippel retweeted
Original Article by @jdppel et al.: AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics nejm.ai/3YwvUZz
#ArtificialIntelligence
ALT Figure 4. Heatmap Visualization of Anomaly Detection Results (Outlier Exposure Approach).
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Jonas Dippel retweeted
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
ALT Figure 2. Clinical Use Case and Overview of Anomaly Detection Approach.
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Jonas Dippel retweeted
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
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Jonas Dippel retweeted
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/scia…
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🧵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/10.1056…
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🙏 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.
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Jonas Dippel retweeted
Why do some memories fade in seconds, while others stay with us for life? Working Memory (WM) holds info for just moments, but certain bits manage to stick around and make it into Long-Term Memory (LTM). In our new ⚡️preprint⚡️, we examined what helps these memories stick. 1/
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Jonas Dippel retweeted
The #aignostics team is heading to #ASCO2024!
Interested in hearing about how we’re transforming precision medicine with our industry-leading foundation model and advanced machine learning algorithms? Let’s connect on June 2-3: lnkd.in/e7eTNxEw
#ASCO24 #PrecisionMedicine
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Jonas Dippel retweeted
🚨 We have 2 open #PhD positions!
Come join us @TUBerlin. We offer cutting edge #research ranging from #XAI over #Probabilistic #Modelling to #AI4Science 🧬
Check out our research profile 👉 t1p.de/uetq8
Apply here 👉 t1p.de/tjunf
#AI #MachineLearning
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Jonas Dippel retweeted
The camera-ready version is now available on arXiv! 🔥 We've updated our manuscript with results for a few interesting experiments that were suggested by the reviewers during the rebuttal period. See the thread below👇
Happy to announce that this wonderful collaborative effort was accepted to @NeurIPSConf! Congrats team! Stay tuned for cam ready 🔥🧠🤖
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Jonas Dippel retweeted
Congrats to the team. The paper “Improving neural network representations using human similarity judgments” will be presented at the @NeurIPSConf.
ALT The paper “Improving neural network representations using human similarity judgments” will be presented at the thirty-seventh Conference on Neural Information Processing Systems. The paper demonstrates that neural network representations can be improved by supervising them with human behavioral data.
🚨Beep beep 🚨 Have you ever been wondering about how to use human similarity judgments for improving neural network representations? We have something for you! We found a transform that improves representational alignment and downstream task performance!
arxiv.org/abs/2306.04507
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Jonas Dippel retweeted
🎇 We have another opening for a PhD student position in the @bifoldberlin agility project 'LungCAIRE' with Charité on multimodal data representations for lung cancer relapse prediction and related use cases:
jobs.tu-berlin.de/en/job-pos…
(application deadline: Sep 08, 2023)
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We built a new website for our lab🧑💻 Check out researchers, publications, courses and software from our group!
We have a new website 🎉
Thanks to all the people who put countless hours into making it look as amazing as it does!
Want to learn more about our team, #research, open positions and offered courses?
Check it out 👉 web.ml.tu-berlin.de
#MachineLearning #Berlin
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Jonas Dippel retweeted
Come pursue your PhD and work with us @TUBerlin @bifoldberlin with @MPIWG @M_Valleriani @ml_tuberlin
Full-time Research Assistant position exploring the intersection of #MachineLearning and the #DigitalHumanities: 📚🔭💻🔎
jobs.tu-berlin.de/en/job-pos…
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