World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
3681
World Ranking
9915
National Ranking
4163

Overview

Axel Wismüller is affiliated with the University of Rochester in the United States. Their research primarily focuses on interdisciplinary fields spanning Medicine, Neuroscience, and Computer Science. Within these main areas, they have contributed extensively to subfields such as Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology, and Health Informatics.

Their work addresses various specialized topics, including:

  • Functional Brain Connectivity Studies
  • Neural dynamics and brain function
  • Artificial Intelligence in Healthcare and Education
  • COVID-19 and healthcare impacts
  • Advanced MRI Techniques and Applications
  • Advanced Neuroimaging Techniques and Applications
  • Bayesian Modeling and Causal Inference

Wismüller has a number of publications in high-visibility venues, with a strong presence in the following:

  • arXiv (Cornell University)
  • Medical Imaging 2020: Computer-Aided Diagnosis
  • Scientific Reports
  • Journal of NeuroVirology
  • 2021 29th European Signal Processing Conference (EUSIPCO)

Some of their noteworthy papers include:

  • Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data, 2021, Scientific Reports
  • Detecting cognitive impairment in HIV-infected individuals using mutual connectivity analysis of resting state functional MRI, 2020, Journal of NeuroVirology
  • Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments, 2021, 2021 29th European Signal Processing Conference (EUSIPCO)
  • Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks, 2020, arXiv (Cornell University)
  • Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data, 2020, arXiv (Cornell University)

Collaborative efforts have been a significant aspect of their career. Frequent co-authors include:

  • M. Ali Vosoughi
  • Adora M. DSouza
  • Anas Z. Abidin
  • Larry Stockmaster
  • Ali Vosoughi

Best Publications

  • Cluster Analysis of Biomedical Image Time-Series

    Axel Wismüller;Oliver Lange;Dominik R. Dersch;Gerda L. Leinsinger

  • MRI tumor segmentation with densely connected 3D CNN

    Lele Chen;Yue Wu;Adora M. DSouza;Anas Z. Abidin

  • Classification of Small Lesions in Breast MRI: Evaluating The Role of Dynamically Extracted Texture Features Through Feature Selection.

    Mahesh B. Nagarajan;Markus B. Huber;Thomas Schlossbauer;Gerda Leinsinger

  • Local exponential stability of competitive neural networks with different time scales

    Anke Meyer-Bäse;Sergei S. Pilyugin;Axel Wismüller;Simon Foo

  • Quantitative Comparison of Automatic and Interactive Methods for MRI–SPECT Image Registration of the Brain Based on 3-Dimensional Calculation of Error

    Thomas Pfluger;Christian Vollmar;Axel Wismüller;Stefan Dresel

  • Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

    Mahesh B. Nagarajan;Markus B. Huber;Thomas Schlossbauer;Gerda Leinsinger

  • Fully automated biomedical image segmentation by self-organized model adaptation

    Axel Wismüller;Frank Vietze;Johannes Behrends;Anke Meyer-Baese

  • The deformable feature map - a novel neurocomputing algorithm for adaptive plasticity in pattern analysis

    Axel Wismüller;Frank Vietze;Dominik R. Dersch;Johannes Behrends

  • Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

    Gerda Leinsinger;Thomas Schlossbauer;Michael Scherr;Oliver Lange

  • Segmentation with neural networks

    Axel Wismüller;Frank Vietze;Dominik R. Dersch

  • Prediction of Biomechanical Properties of Trabecular Bone in MR Images With Geometric Features and Support Vector Regression

    Markus B Huber;Sarah L Lancianese;M B Nagarajan;I Z Ikpot

  • Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series

    A. Wismuller;A. Meyer-Baese;O. Lange;M.F. Reiser

  • Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data

    Kerstin Bunte;Barbara Hammer;Axel Wismüller;Michael Biehl

  • Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization.

    Thomas Schlossbauer;Gerda Leinsinger;Axel Wismuller;Oliver Lange

  • Performance of topological texture features to classify fibrotic interstitial lung disease patterns.

    Markus B. Huber;Mahesh B. Nagarajan;Gerda Leinsinger;Roger Eibel

  • Neighbor embedding XOM for dimension reduction and visualization

    Kerstin Bunte;Barbara Hammer;Thomas Villmann;Michael Biehl

  • Computer-Aided Diagnosis in Phase Contrast Imaging X-Ray Computed Tomography for Quantitative Characterization of ex vivo Human Patellar Cartilage

    Mahesh B. Nagarajan;Paola Coan;Markus B. Huber;Paul C. Diemoz

  • Medical image compression using topology-preserving neural networks

    Anke Meyer-Bäse;Karsten Jancke;Axel Wismüller;Simon Foo

  • Tumor feature visualization with unsupervised learning.

    Tim Wilhelm Nattkemper;A Wismuller

  • Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

    Anas Z. Abidin;Botao Deng;Adora M. DSouza;Mahesh B. Nagarajan

  • Alteration of brain network topology in HIV-associated neurocognitive disorder: A novel functional connectivity perspective.

    Anas Z. Abidin;Adora M. DSouza;Mahesh B. Nagarajan;Lu Wang

  • Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization

    Kerstin Bunte;Barbara Hammer;Thomas Villmann;Michael Biehl

  • The Exploration Machine --- A Novel Method for Data Visualization

    Axel Wismüller

  • A Neural Network Approach to Functional MRI Pattern Analysis — Clustering of Time-Series by Hierarchical Vector Quantization

    Axel Wismüller;Dominik R. Dersch;Bernadette Lipinski;Klaus Hahn

Frequent Co-Authors

Dorothee P. Auer
Dorothee P. Auer University of Nottingham
Tim Wilhelm Nattkemper
Tim Wilhelm Nattkemper Bielefeld University
Helge Ritter
Helge Ritter Bielefeld University
Thomas Villmann
Thomas Villmann Hochschule Mittweida
Barbara Hammer
Barbara Hammer Bielefeld University
Thomas D. Otto
Thomas D. Otto University of Glasgow
John J. Foxe
John J. Foxe University of Rochester
Fabian J. Theis
Fabian J. Theis Technical University of Munich
Sharmila Majumdar
Sharmila Majumdar University of California, San Francisco
Thomas Martinetz
Thomas Martinetz University of Lübeck

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