Carsten T. Lüth

PhD Student @ IML Group at DKFZ / Helmholtz Imaging

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Heidelberg, Germany

I am a deep learning researcher currently a PhD Student in the Interactive Machine Learning Group at the DKFZ / Helmholtz Imaging headed by Dr. Paul Jäger. Currently, my main area of research is Deep Active Learning, which aims at reducing the annotation effort for supervised training by selecting the most informative samples for annotation. To find these most informative samples, I am also highly interested in uncertainty estimation, generative modeling. Further, I believe that the combination of Active Learning with alternate techniques to reduce the amount of annotated data for training algorithms (Semi- and Self-Supervised Learning and Foundation Models) will allow to use the power of deep learning for many small scale projects and research questions.

On the side, I am one of the organizers of heidelberg.ai, a community with over 2400 members, where we host events to encourage scientific discussions about artificial intelligence.

Further, I have past working experiences and am still interested in the fields of Deep Generative Methods and Anomaly Detection. Until recently, I studied physics at the University of Heidelberg, focusing on Computational Physics.\

Generally, I like to read widely because: “You never know what you don’t know”. But Deep Learning, Physics, Psychology, Programming, and Statistics have captured my mind for a long time.

Also, during my free time, I never let a chance pass by to entertain myself with some fiction, just to let my imagination run wild. Whenever I am neither reading nor working, I do something like wakeboarding, bouldering, playing piano, cooking, meeting friends, or programming.

I also maintain an occasional blog—feel free to check it out and explore my thoughts and insights on various subjects. Thank you for visiting my page, and I look forward to connecting and sharing ideas with you.


selected publications

  1. Guided Image Generation with Conditional Invertible Neural Networks
    Ardizzone, Lynton,  Lüth, Carsten, Kruse, Jakob, Rother, Carsten, and Köthe, Ullrich
    arXiv:1907.02392 [cs] Jul 2019
  2. A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
    Jaeger, Paul F,  Lüth, Carsten T., Klein, Lukas, and Bungert, Till J.
    ICLR 2023 Jan 2023
  3. cOOpD: Reformulating COPD Classification on Chest CT Scans as Anomaly Detection Using Contrastive Representations
    Almeida, Silvia D,  Lüth, Carsten T, Norajitra, Tobias, Wald, Tassilo, Nolden, Marco, Jäger, Paul F, Heussel, Claus P, Biederer, Jürgen, Weinheimer, Oliver, and Maier-Hein, Klaus H
    In MICCAI 2023 Oct 2023
  4. Navigating the pitfalls of active learning evaluation: A systematic framework for meaningful performance assessment
    Lüth, Carsten T, Bungert, Till J, Klein, Lukas, and Jaeger, Paul F
    In NeurIPS 2023 Dez 2023