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Mattias P. Heinrich

Mattias P. Heinrich

D-Index & Metrics

Computer Science

D-Index
37
Citations
13495
World Ranking
10455
National Ranking
523

Overview

Mattias P. Heinrich is affiliated with the University of Lübeck in Germany. Their research spans multiple disciplines primarily focused on the intersection of medicine, computer science, and engineering.

The main fields of their work include:

  • Medicine
  • Computer Science
  • Engineering

Within these fields, Heinrich's work covers several subfields, such as:

  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Biomedical Engineering
  • Artificial Intelligence
  • Computational Mechanics

Their research topics reflect an emphasis on medical imaging and machine learning applications, including:

  • Medical Image Segmentation Techniques
  • Advanced Neural Network Applications
  • Medical Imaging and Analysis
  • Radiomics and Machine Learning in Medical Imaging
  • Medical Imaging Techniques and Applications
  • COVID-19 diagnosis using AI
  • 3D Shape Modeling and Analysis

Recent published papers authored or co-authored by Mattias P. Heinrich include:

  • "Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning," 2021, npj Digital Medicine
  • "GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs," 2021, IEEE Transactions on Medical Imaging
  • "Attention-augmented U-Net (AA-U-Net) for semantic segmentation," 2022, Signal Image and Video Processing
  • "Weakly-supervised learning of multi-modal features for regularised iterative descent in 3D image registration," 2020, Medical Image Analysis
  • "Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation," 2021, Journal of Biomedical Informatics

Frequent co-authors collaborating with Heinrich include:

  • Lasse Hansen
  • Alexander Bigalke
  • Hanna Siebert
  • Christoph Großbröhmer
  • Ron Keuth

Their scientific contributions have been published regularly in various venues, with notable frequent publications including:

  • arXiv (Cornell University)
  • International Journal of Computer Assisted Radiology and Surgery
  • Sensors
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Transactions on Medical Imaging

In addition to articles, Heinrich has contributed to book publications, including a volume published by Springer Science+Business Media titled "Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data" (2021).

Best Publications

  • Attention U-Net: Learning Where to Look for the Pancreas

    Ozan Oktay;Jo Schlemper;Loïc Le Folgoc;Matthew C. H. Lee

  • Attention gated networks: Learning to leverage salient regions in medical images.

    Jo Schlemper;Ozan Oktay;Michiel Schaap;Mattias P. Heinrich

  • Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

    Mattias P. Heinrich;Mark Jenkinson;Manav Bhushan;Tahreema Matin

  • 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

  • Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

    K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus

  • MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

    H. P. Heinrich;M. Jenkinson;M. Brady;J. A. Schnabel

  • Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

    Ozan Oktay;Enzo Ferrante;Konstantinos Kamnitsas;Mattias Heinrich

  • Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

    Xiahai Zhuang;Lei Li;Christian Payer;Darko Stern

  • Non-local shape descriptor: a new similarity metric for deformable multi-modal registration

    Mattias P. Heinrich;Mark Jenkinson;Manav Bhushan;Tahreema Matin

  • Evaluating fibre orientation dispersion in white matter: Comparison of diffusion MRI, histology and polarized light imaging.

    Jeroen Mollink;Jeroen Mollink;Michiel Kleinnijenhuis;Anne-Marie van Cappellen van Walsum;Stamatios N. Sotiropoulos;Stamatios N. Sotiropoulos

  • Towards Realtime Multimodal Fusion for Image-Guided Interventions Using Self-similarities.

    Mattias Paul Heinrich;Mark Jenkinson;Bartlomiej W. Papież;Sir Michael Brady

  • Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks

    Oscar Jimenez-del-Toro;Henning Muller;Markus Krenn;Katharina Gruenberg

  • ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

    Stefan Winzeck;Arsany Hakim;Richard McKinley;José A. A. D. S. R. Pinto

  • Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT

    Zhoubing Xu;Christopher P. Lee;Mattias P. Heinrich;Marc Modat

  • Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration

    Jan Ruhaak;Thomas Polzin;Stefan Heldmann;Ivor J. A. Simpson

  • An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration

    Bartłomiej W. Papież;Mattias P. Heinrich;Jérome Fehrenbach;Laurent Risser

  • Deformable image registration by combining uncertainty estimates from supervoxel belief propagation

    Mattias P. Heinrich;Ivor J.A. Simpson;BartŁomiej W. Papież;Sir Michael Brady

  • Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

    Ozan Oktay;Wenjia Bai;Ricardo Guerrero;Martin Rajchl

  • Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising

    Mattias P. Heinrich;Maik Stille;Thorsten M. Buzug

  • Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning.

    Bernhard Kainz;Mattias P. Heinrich;Antonios Makropoulos;Jonas Oppenheimer

  • OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions.

    Mattias P. Heinrich;Ozan Oktay;Nassim Bouteldja

Frequent Co-Authors

Julia A. Schnabel
Julia A. Schnabel King's College London
Heinz Handels
Heinz Handels University of Lübeck
Ozan Oktay
Ozan Oktay Imperial College London
Mark Jenkinson
Mark Jenkinson University of Oxford
Bennett A. Landman
Bennett A. Landman Vanderbilt University
Ben Glocker
Ben Glocker Imperial College London
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Bernhard Kainz
Bernhard Kainz Imperial College London
Sebastien Ourselin
Sebastien Ourselin King's College London
Marc Modat
Marc Modat King's College London

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