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Thomas Martinetz

Thomas Martinetz

D-Index & Metrics

Computer Science

D-Index
41
Citations
11606
World Ranking
8632
National Ranking
425

Overview

Thomas Martinetz is affiliated with the University of Lübeck in Germany and specializes in the intersection of computer science and medicine. Their research focuses notably on artificial intelligence and its applications in medical diagnostics and biometrics.

The scientist has contributed extensively to the fields of computer science and medicine, with particular emphasis on:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Radiology, Nuclear Medicine and Imaging
  • Cognitive Neuroscience
  • Signal Processing

The main research topics Thomas Martinetz explores include:

  • Advanced Neural Network Applications
  • COVID-19 diagnosis using AI
  • Anomaly Detection Techniques and Applications
  • Radiomics and Machine Learning in Medical Imaging
  • Biometric Identification and Security
  • Face recognition and analysis
  • Sleep and Wakefulness Research

They have published multiple papers in prominent venues, frequently collaborating with a group of notable co-authors. These frequent collaborators include Hammam Alshazly, Erhardt Barth, Christoph Linse, Lisa Marshall, and Philipp Grüning.

Typical venues for their publications include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Access
  • PLoS ONE
  • Sensors

Selected recent publications by Thomas Martinetz include:

  • "Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning", 2021, Sensors
  • "Deep Convolutional Neural Networks for Unconstrained Ear Recognition", 2020, IEEE Access
  • "COVID-Nets: Deep CNN Architectures for Detecting COVID-19 Using Chest CT Scans", 2021, PeerJ Computer Science
  • "Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework", 2022, Contrast Media & Molecular Imaging
  • "Towards Explainable Ear Recognition Systems Using Deep Residual Networks", 2021, IEEE Access

Best Publications

  • 'Neural-gas' network for vector quantization and its application to time-series prediction

    T.M. Martinetz;S.G. Berkovich;K.J. Schulten

  • Neural computation and self-organizing maps : an introduction

    Helge Ritter;Thomas Martinetz;Klaus Schulten;Daniel Barsky

  • Topology representing networks

    Thomas Martinetz;Thomas Martinetz;Klaus Schulten

  • Neural computation and self-organizing maps - an introduction

    Unknown

  • Variability of eye movements when viewing dynamic natural scenes.

    Michael Dorr;Thomas Martinetz;Karl R. Gegenfurtner;Erhardt Barth

  • Topology-conserving maps for learning visuo-motor-coordination

    H. J. Ritter;T. M. Martinetz;K. J. Schulten

  • Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps

    Thomas Martinetz

  • Topology preservation in self-organizing feature maps: exact definition and measurement

    T. Villmann;R. Der;M. Herrmann;T.M. Martinetz

  • Three-dimensional neural net for learning visuomotor coordination of a robot arm

    T.M. Martinetz;H.J. Ritter;K.J. Schulten

  • AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties.

    Krishna Kumar Kandaswamy;Kuo-Chen Chou;Thomas Martinetz;Steffen Möller

  • Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.

    Hammam A. Alshazly;Christoph Linse;Erhardt Barth;Thomas Martinetz

  • Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

    Oskar Maier;Christoph Schröder;Nils Daniel Forkert;Thomas Martinetz

  • Dynamic fitness landscapes in molecular evolution

    Claus O. Wilke;Christopher Ronnewinkel;Thomas Martinetz

  • Simple Method for High-Performance Digit Recognition Based on Sparse Coding

    K. Labusch;E. Barth;T. Martinetz

  • Deep convolutional neural networks as generic feature extractors

    Lars Hertel;Erhardt Barth;Thomas Kaster;Thomas Martinetz

  • Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke

    Helge Ritter;Thomas Martinetz;Klaus Schulten

  • Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition.

    Krishna Kumar Kandaswamy;Ganesan Pugalenthi;Steffen Moller;Enno Hartmann

  • Sparse Coding Neural Gas: Learning of overcomplete data representations

    Kai Labusch;Erhardt Barth;Thomas Martinetz

  • Timing matters: open-loop stimulation does not improve overnight consolidation of word pairs in humans.

    Arne Weigenand;Matthias Mölle;Friederike Werner;Thomas Martinetz

  • Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition.

    Hammam A. Alshazly;Christoph Linse;Erhardt Barth;Thomas Martinetz

  • Remote Eye Tracking: State of the Art and Directions for Future Development

    Martin Böhme;André Meyer;Thomas Martinetz;Erhardt Barth

Frequent Co-Authors

Erhardt Barth
Erhardt Barth University of Lübeck
Claus O. Wilke
Claus O. Wilke The University of Texas at Austin
Helge Ritter
Helge Ritter Bielefeld University
Karl R. Gegenfurtner
Karl R. Gegenfurtner University of Giessen
Jan Born
Jan Born University of Tübingen
Lisa Marshall
Lisa Marshall University of Lübeck
Thomas Villmann
Thomas Villmann Hochschule Mittweida
Enno Hartmann
Enno Hartmann University of Lübeck
Haibo He
Haibo He University of Rhode Island

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