Prof. Dr. Peter Schüffler


Computational Pathology​​​​​​​

Academic Career and Research Areas

Prof. Schüffler's (*1983) field of research is the area of digital and computational pathology. This includes novel machine learning approaches for the detection, segmentation and grading of cancer in pathology images, prediction of prognostic markers and outcome prediction (e.g. treatment response). Further, he investigates the efficient visualization of high-resolution digital pathology images, automated QA, new ergonomics for pathologists, and holistic integration of digital systems for clinics, research and education.

Prof. Schüffler received his BSc and MSc in Computational Biology at the Saarland University and the MPI, Saarbrücken, Germany. In 2015, he graduated his doctoral studies in machine learning for medical image data analysis at the ETH Zurich, Switzerland. He deepened his expertise in digital and computational pathology as a Postdoc and Sr. ML Scientist at the Memorial Sloan Kettering Cancer Center New York, USA, where he co-founded Paige. In 2021, Prof. Schüffler was appointed to the professorship for computational pathology at TUM.


    • Best Paper Award: Schüffler PJ et al. Computer Aided Crohn’s Disease Severity Assessment in MRI. VIGOR++ Workshop (2014)
    • Outstanding Paper Award: Schüffler PJ et al. A Model Development Pipeline for Crohn’s Disease Severity Assessment from Magnetic Resonance Images. Abdominal Imaging. Computation and Clinical Applications (2013)

    Hanna MG, Reuter V, Ardon O, Kim D, Sirintrapun SJ, Schüffler PJ et al.: "Validation of a digital pathology system including remote review during the COVID-19 pandemic". Modern Pathology. 2020; vol. 33: p. 2115–2127.


    Ho DJ, Agaram NP, Schüffler PJ et al.: "Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment". In: Martel, Abolmaesumi, Stoyanov, Mateus, Zuluaga, Zhou, Racoceanu and Joskowicz (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI. Springer International Publishing. 2020; vol. 12265, p. 540-549.


    Hanna M, Reuter V, Hameed MR, Tan LK, Chiang S, Sigel C, Hollmann T, Giri D, Samboy J, Moradel C, Rosado A, Otilano III JR, England C, Corsale L, Stamelos E, Yagi Y, Schüffler PJ, et al.: "Whole slide imaging equivalency and efficiency study: experience at a large academic center". Modern Pathology. 2019; vol. 32, p. 916–928.


    Puylaert CAJ*, Schüffler PJ* et al.: "Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project)". Academic Radiology. 2018; vol. 25, 8, p. 1038-1045.
    *equal contribution


    Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, Schüffler PJ, et al.: "Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry". Nature methods. 2014; vol. 11, p. 417-22.