Research Group Peter Schüffler
Peter Schüffler
Prof. Dr.
Associate
Peter Schüffler
is Professor for Computational Pathology at TU Munich.
His 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.
Team members @MCML
PostDocs
Reza Nasirigerdeh
Dr.
→ Group Peter Schüffler
Computational Pathology
PhD Students
Christian Grashei
→ Group Peter Schüffler
Computational Pathology
Azar Kazemi
→ Group Peter Schüffler
Computational Pathology
Jingsong Liu
→ Group Peter Schüffler
Computational Pathology
Oskar Thaeter
→ Group Peter Schüffler
Computational Pathology
Recent News @MCML
Publications @MCML
2025
From histology to diagnosis: Leveraging pathology foundation models for glioma classification.
Computers in Biology and Medicine 197.Part A. Oct. 2025. DOI
HASD: Hierarchical Adaption for pathology Slide-level Domain-shift.
MICCAI 2025 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025. DOI
From Pixels to Pathology: Restoration Diffusion for Diagnostic-Consistent Virtual IHC.
Preprint (Aug. 2025). arXiv
Attention Pooling Enhances NCA-based Classification of Microscopy Images.
Preprint (Aug. 2025). arXiv
Unlocking the potential of digital pathology: Novel baselines for compression.
Journal of Pathology Informatics 17.100421. Apr. 2025. DOI
From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis.
Preprint (Mar. 2025). arXiv GitHub
An equivalency and efficiency study for one year digital pathology for clinical routine diagnostics in an accredited tertiary academic center.
Virchows Archiv. Feb. 2025. DOI
2024
Learned Image Compression for HE-Stained Histopathological Images via Stain Deconvolution.
MOVI @MICCAI 2024 - 2nd International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. Marrakesh, Morocco, Oct 06-10, 2024. DOI GitHub
A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes.
Computers in Biology and Medicine 173. May. 2024. DOI
Künstliche Intelligenz in der Pathologie – wie, wo und warum?
Die Pathologie. Mar. 2024. DOI
©all images: LMU | TUM