Prof. Arndt Hartmann

Pathological Institute

Research projects

  • Pathology and Molecular Pathology with research focus on bladder cancer, renal cell carcinoma, head and neck and breast cancer.
  • Prognostic and predictive molecular tumour markers.
  • Digital pathology and devolopment of AI prediction models for diagnosis, molecular changes and therapy prediction.
  • Characterization of the immunological tumor microenvironment and correlation to response to immunooncological therapy.

  • D039: EMT specific splicing mediates ferroptosis sensitivity

    (FAU Funds)

    Term: 1. July 2023 - 31. December 2025
    We have demonstrated that the EMT-activator ZEB1 provides cancer cells not only with aberrant motility, but also with survival traits enabling tumor progression, metastasis and drug resistance. Our aim is to eliminate these aggressive ‘untargetable’ EMT-state cancer cells, which strikingly show a high sensitivity to ferroptotic cell death. In this project, we want to elucidate the molecular basis of ZEB1 – associated ferroptosis sensitivity to exploit it as a novel therapeutic target.
  • J101: AI-based support for histopathology evaluation of autoinflammatory and malignant GI diseases

    (FAU Funds)

    Term: 1. January 2023 - 31. December 2025
    The proposed projects aims at using methods from AI-based image analysis to evaluate histopathologic samples from the field of gastrointestinal pathology. Specifically, samples from patients with inflammatory bowel diseases and malignancies of the colorectum will be evaluated. It is the aim of the project to develop algorithms that quantify and detect specific morphologic properties of these samples and integrate them with other data modalities.

2025

  • Öttl, M., Mei, S., Wilm, F., Steenpaß, J., Rübner, M., Hartmann, A.,... Breininger, K. (2025). Analysing Diffusion Segmentation for Medical Images. (Unpublished, Submitted).
  • Öttl, M., Wilm, F., Steenpaß, J., Qiu, J., Rübner, M., Hartmann, A.,... Breininger, K. (2025). Style-Extracting Diffusion Models for Semi-supervised Histopathology Segmentation. In Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 236-252). Milan, IT: Springer Science and Business Media Deutschland GmbH.

2024

2023

2022

2021

2020

Related Research Fields

Contact: