L. Papp

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

J. L. Herraiz, D. Cioni, José Manuel Udías, 2022, Cancers.

Automated data preparation for in vivo tumor characterization with machine learning

Clemens P. Spielvogel, T. Beyer, M. Hacker, 2022, Frontiers in Oncology.

Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers

R. Wille, W. Drexler, L. Papp, 2022, 2206.01573.

Error mitigation for a clinical data classification on the IonQ and the IBM quantum computers

R. Wille, W. Drexler, L. Papp, 2022 .

Clinical data classification with noisy intermediate scale quantum computers

Robert Wille, Wolfgang Drexler, Stefan Hillmich, 2021, Scientific Reports.

Positron range in combination with point-spread-function correction: an evaluation of different implementations for [124I]-PET imaging

J. L. Herraiz, T. Beyer, J. Cal-Gonzalez, 2022, EJNMMI Physics.

Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts

M. Hacker, S. Rasul, M. Grahovac, 2023, European Journal of Nuclear Medicine and Molecular Imaging.

Error mitigation enables PET radiomic cancer characterization on quantum computers

Clemens P. Spielvogel, T. Traub-Weidinger, S. Hillmich, 2023, European Journal of Nuclear Medicine and Molecular Imaging.

DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters

Clemens P. Spielvogel, M. Grahovac, L. Papp, 2023, Neural Networks.