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Rising Stars
2025

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Rising Stars

D-Index
62
Citations
27918
World Ranking
143
National Ranking
6

Computer Science

D-Index
62
Citations
40902
World Ranking
2818
National Ranking
127

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Klaus H. Maier-Hein is affiliated with the German Cancer Research Center in Germany. Their research spans multiple domains within Medicine and Computer Science, with a particular emphasis on medical imaging and artificial intelligence applications in healthcare.

Their work covers significant subfields including Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, and Biomedical Engineering.

Key topics explored in their publications encompass:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Artificial Intelligence in Healthcare and Education
  • Medical Imaging and Analysis
  • Medical Image Segmentation Techniques

Frequent co-authors collaborating with Klaus H. Maier-Hein include:

  • Fabian Isensee
  • Peter Neher
  • Paul F. Jäger
  • Michael Baumgartner
  • Jens Kleesiek

Publication venues with multiple works authored by Klaus H. Maier-Hein are:

  • arXiv (Cornell University)
  • Medical Image Analysis
  • Scientific Reports
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature Communications

Recent papers include:

  • "The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping", 2020, Radiology
  • "The Medical Segmentation Decathlon", 2022, Nature Communications
  • "The Liver Tumor Segmentation Benchmark (LiTS)", 2022, Medical Image Analysis
  • "CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation", 2020, Medical Image Analysis
  • "MONAI: An open-source framework for deep learning in healthcare", 2022, arXiv (Cornell University)

Klaus H. Maier-Hein has also contributed to several book publications with Springer Nature, including the series "Bildverarbeitung für die Medizin," with editions from 2020 through 2024.

Best Publications

  • Automated Design of Deep Learning Methods for Biomedical Image Segmentation

    Fabian Isensee;Paul F. Jäger;Simon A. A. Kohl;Jens Petersen

  • nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

    Fabian Isensee;Fabian Isensee;Paul F Jaeger;Simon A A Kohl;Jens Petersen;Jens Petersen

  • The future of digital health with federated learning

    Nicola Rieke;Nicola Rieke;Jonny Hancox;Wenqi Li;Fausto Milletari

  • The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

    Alex Zwanenburg;Alex Zwanenburg;Martin Vallières;Mahmoud A. Abdalah;Hugo J. W. L. Aerts;Hugo J. W. L. Aerts

  • Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

    Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • The challenge of mapping the human connectome based on diffusion tractography

    Klaus H. Maier-Hein;Peter F. Neher;Jean-Christophe Houde;Marc-Alexandre Cote

  • CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

    A. Emre Kavur;N. Sinem Gezer;Mustafa Barış;Sinem Aslan

  • Automated brain extraction of multisequence MRI using artificial neural networks.

    Fabian Isensee;Marianne Schell;Irada Pflueger;Gianluca Brugnara

  • TractSeg - Fast and accurate white matter tract segmentation

    Jakob Wasserthal;Jakob Wasserthal;Peter F. Neher;Klaus H. Maier-Hein;Klaus H. Maier-Hein

  • Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

    Fabian Isensee;Jens Petersen;Andre Klein;David Zimmerer

  • Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

    Jens Kleesiek;Gregor Urban;Alexander Hubert;Daniel Schwarz

  • ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

    Oskar Maier;Bjoern H. Menze;Janina von der Gablentz;Levin Häni

  • Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

    Fabian Isensee;Philipp Kickingereder;Wolfgang Wick;Martin Bendszus

  • The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development.

    Marco Nolden;Sascha Zelzer;Alexander Seitel;Diana Wald

  • Methodological considerations on tract-based spatial statistics (TBSS).

    Michael Bach;Frederik B. Laun;Alexander Leemans;Chantal M. W. Tax

  • The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.

    Nicholas Heller;Fabian Isensee;Klaus H. Maier-Hein;Xiaoshuai Hou

  • Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

    Philipp Kickingereder;Fabian Isensee;Irada Tursunova;Jens Petersen

  • nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

    Fabian Isensee;Jens Petersen;Andre Klein;David Zimmerer

  • No New-Net

    Fabian Isensee;Philipp Kickingereder;Wolfgang Wick;Martin Bendszus

Frequent Co-Authors

Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center
Robert Christian Wolf
Robert Christian Wolf Heidelberg University
Sabine Heiland
Sabine Heiland University Hospital Heidelberg
Maxime Descoteaux
Maxime Descoteaux Université de Sherbrooke
Stefanie Speidel
Stefanie Speidel National Center for Tumor Diseases
Bjoern H. Menze
Bjoern H. Menze University of Zurich
Hans-Peter Meinzer
Hans-Peter Meinzer German Cancer Research Center
Bennett A. Landman
Bennett A. Landman Vanderbilt University
Tal Arbel
Tal Arbel McGill University
Russell T. Shinohara
Russell T. Shinohara University of Pennsylvania

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