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Link to Peter Schüffler

Peter Schüffler

Prof. Dr.

Associate

Computational Pathology

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

Link to Christian Grashei

Christian Grashei

Computational Pathology

Link to Jingsong Liu

Jingsong Liu

Computational Pathology

Publications @MCML

[1]
A. Kazemi, A. Rasouli-Saravani, M. Gharib, T. Albuquerque, S. Eslami and P. J. Schüffler.
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.
Abstract

The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.

MCML Authors
Link to Peter Schüffler

Peter Schüffler

Prof. Dr.

Computational Pathology