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Selected publications

  1. Kerol Djoumessi , Samuel Ofosu Mensah, Philipp Berens , "A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification", IMIMIC-MICCAI Workshop, Oral presentation
  2. Samuel Ofosu Mensah, Kerol Djoumessi , Philipp Berens , "Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning", MICCAI 2025.
  3. Samuel Ofosu Mensah, Jonas Neubauer, Murat Seçkin Ayhan, Kerol Djoumessi , Lisa Koch, Mehmet Murat Uzel, Faik Gelisken, Philipp Berens , "Clinically Interpretable Deep Learning via Sparse BagNets for Epiretinal Membrane and Related Pathology Detection", medrxiv., Jun 2025.
  4. Kerol Djoumessi , Ziwei Huang, Laura Kuhlewein, Annekatrin Rickmann, Natalia Simon, Lisa M. Koch, Philipp Berens , "An Inherently Interpretable AI model improves Screening Speed and Accuracy for Early Diabetic Retinopathy", Plos Digital Health, April 2025.
  5. Kerol Djoumessi , Philipp Berens , "Soft-CAM: Making black box models self-explainable for high-stakes decisions" , Accepted at MIDL 2026.
  6. Kerol Djoumessi , Bubacarr Bah, Laura Kuhlewein, Philipp Berens, Lisa M. Koch , "This actually looks like that: Proto-BagNets for local and global interpretability-by-design", accepted to the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), Marrakesh, Morocco, October 6-10, 2024.
  7. Julius Gervelmeyer, Sarah Müller, Kerol Djoumessi , David Merle, Simon Clark, Lisa M. Koch, Philipp Berens , "Interpretable-by-design Deep Survival Analysis for Disease Progression Modeling", accepted to the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), Marrakesh, Morocco, October 6-10, 2024.
  8. Kerol Djoumessi , Indu Ilanchezian, Laura Kuhlewein, Hanna Faber, Christian F. Baumgartner, Bubacarr Bah, Philipp Berens, Lisa M. Koch , "Sparse Activations for Interpretable Disease Grading", Proceedings of the 6th Medical Imaging with Deep Learning (MIDL 2023), Nashville USA, July 10-12, 2023. [Oral presentation]