Publications

Selected papers from our group, highlighting key contributions and interdisciplinary collaborations.

Balcerak M, Amiranashvili T, Wagner A, Weidner J, Karnakov P, Paetzold JC, Ezhov I, Koumoutsakos P, Wiestler B*, Menze BH*.
Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization.
NeurIPS, 2024

Ziegenfeuter J, Delbridge C, Bernhardt D, Gempt J, Schmidt-Graf F, Hedderich D, Griessmair M, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Metz M, Wiestler B.
Resolving spatial response heterogeneity in glioblastoma.
EJNMMI, 2024

Jian B, Pan J, Ghahremani M, Rueckert D, Wachinger C*, Wiestler B*.
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration.
WBIR @ MICCAI, 2024

Scholz D, Erdur A, Buchner J, Peeken JC, Rueckert D, Wiestler B.
Imbalance-aware loss functions improve medical image classification.
MIDL, 2024

Varma A, Shit S, Prabhakar C, Scholz D, Li H, Menze BH, Rueckert D, Wiestler B.
VariViT: A Vision Transformer for Variable Image Sizes.
MIDL, 2024

Meissen F, Breuer S, Knolle M, Buyx A, Mueller R, Kaissis G, Wiestler B*, Rueckert D*.
(Predictable) performance bias in unsupervised anomaly detection.
EBioMedicine, 2024

McGinnis J, Shit S, Li H, Sideri-Lampretsa V, Graf R, Dannecker M, Pan J, Stolt-Ansó N, Mühlau M, Kirschke J, Rueckert D, Wiestler B.
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations.
MICCAI, 2023

Prabhakar C, Li H, Paetzold J, Loehr T, Niu C, Mühlau M, Rueckert D, Wiestler B*, Menze BH*.
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images.
MICCAI, 2023

Prabhakar C, Li H, Yang J, Shit S, Wiestler B*, Menze BH*.
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations.
MIDL, 2023

Kofler F, Shit S, Ezhov I, Fidon L, Horvath I, Al-Maskari R, Li H, Bhatia H, Loehr T, Piraud M, Ertuerk A, Kirschke J, Peeken J, Vercauteren T, Zimmer C, Wiestler B*, Menze BH*.
blob loss: instance imbalance aware loss functions for semantic segmentation.
IPMI, 2023

Ezhov I, Scibilia K, Franitza K, Steinbauer F, Shit S, Zimmer L, Lipkova J, Kofler F, Paetzold JC, Canalini L, Waldmannstetter D, Menten M, Metz M, Wiestler B*, Menze BH*.
Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling.
Medical Image Analysis, 2022

Bercea CI, Wiestler B, Rueckert D, Albarqouni S.
Federated disentangled representation learning for unsupervised brain anomaly detection.
Nature Machine Intelligence, 2022

Gempt J, Withake F, Aftahy AK, Meyer HS, Barz M, Delbridge C, Liesche-Starnecker F, Prokop G, Pfarr N, Schlegel J, Meyer B, Zimmer C, Menze BH*, Wiestler B*.
Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: implications for biopsy targeting, classification and therapy.
ESMO Open, 2022

Ezhov I, Mot T, Shit S, Lipkova J, Paetzold JC, Kofler F, Pellegrini C, Kollovieh M, Navarro F, Li H, Metz M, Wiestler B, Menze BH.
Geometry-aware neural solver for fast Bayesian calibration of brain tumor models.
IEEE TMI, 2021

Thomas MF, Kofler F, Grundl L, Finck T, Li H, Zimmer C, Menze BH, Wiestler B.
Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans.
Investigative Radiology, 2021

Paprottka KJ, Kleiner S, Preibisch C, Kofler F, Schmidt-Graf F, Delbridge C, Bernhardt D, Combs SE, Gempt J, Meyer B, Zimmer C, Menze BH, Yakushev I, Kirschke JS, Wiestler B.
Fully automated analysis combining 18F-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression.
EJNMMI, 2021

Finck T, Schinz D, Grundl L, Eisawy R, Yigitsoy M, Moosbauer J, Pfister F, Wiestler B.
Automated Pathology Detection and Patient Triage in Routinely Acquired Head Computed Tomography Scans.
Investigative Radiology, 2021

Baur C*, Wiestler B*, Mühlau M, Zimmer C, Navab N, Albarqouni S.
Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI.
Radiology AI, 2021

Metz MC, Molina-Romero M, Lipkova J, Gempt J, Liesche-Starnecker F, Eichinger P, Grundl L, Menze BH, Combs SE, Zimmer C, Wiestler B.
Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression.
Cancers, 2020

Li H, Paetzold J, Sekuboyina A, Kofler F, Zhang J, Kirschke JS, Wiestler B, Menze BH.
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis.
MICCAI, 2019

Baur C, Wiestler B, Albarqouni S, Navab N.
Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation.
MIDL, 2019

Eichinger P, Schön S, Pongratz V, Wiestler H, Zhang H, Bussas M, Hoshi MM, Kirschke JS, Berthele A, Zimmer C, Hemmer B, Mühlau M, Wiestler B.
Accuracy of Unenhanced MRI in the Detection of New Brain Lesions in Multiple Sclerosis.
Radiology, 2019

Lipkova J, Angelikopoulos P, Wu S, Alberts E, Wiestler B, Diehl C, Preibisch C, Pyka T, Combs S, Hadjidoukas P, Van Leemput K, Koumoutsakos P, Lowengrub JS, Menze BH.
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference.
IEEE TMI, 2019

Molina-Romero M, Wiestler B, Gómez PA, Menzel MI, Menze BH.
Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases.
MICCAI, 2018

Zhang H, Alberts E, Pongratz V, Mühlau M, Zimmer C, Wiestler B, Eichinger P.
Predicting conversion from clinically isolated syndrome to multiple sclerosis–An imaging-based machine learning approach.
NeuroImage: Clinical, 2018

Baur C, Wiestler B, Albarqouni S, Navab N.
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images.
BrainLes @ MICCAI, 2018

Eichinger P, Wiestler H, Zhang H, Biberacher V, Kirschke JS, Zimmer C, Mühlau M, Wiestler B.
A novel imaging technique for better detecting new lesions in multiple sclerosis.
J Neurol, 2017

Eichinger P, Alberts E, Delbridge C, Trebeschi S, Valentinitsch A, Bette S, Huber T, Gempt J, Meyer B, Schlegel J, Zimmer C, Kirschke JS, Menze BH, Wiestler B.
Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas.
Scientific Reports, 2017

Alberts E, Tetteh G, Trebeschi S, Bieth M, Valentinitsch A, Wiestler B, Zimmer C, Menze BH.
Multi-modal Image Classification Using Low-Dimensional Texture Features for Genomic Brain Tumor Recognition.
MICGen @ MICCAI, 2017

Osswald M, Jung E, Sahm F, Solecki G, Venkataramani V, Blaes J, Weil S, Horstmann H, Wiestler B, Syed M, Huang L, Ratliff M, Karimian Jazi K, Kurz FT, Schmenger T, Lemke D, Gömmel M, Pauli M, Liao Y, Häring P, Pusch S, Herl V, Steinhäuser C, Krunic D, Jarahian M, Miletic H, Berghoff AS, Griesbeck O, Kalamakis G, Garaschuk O, Preusser M, Weiss S, Liu H, Heiland S, Platten M, Huber PE, Kuner T, von Deimling A, Wick W, Winkler F.
Brain tumour cells interconnect to a functional and resistant network.
Nature, 2015