Automatische Segmentierung der Lungenflügel in CT-Daten. - Matthias Wilms; Jan Ehrhardt; Heinz Handels (Bildverarbeitung für die Medizin, 2011)

Evaluation of Algorithms for Lung Fissure Segmentation in CT Images. - Alexander Schmidt-Richberg; Jan Ehrhardt; Matthias Wilms; René Werner; Heinz Handels (Bildverarbeitung für die Medizin, 2012)

A Diffeomorphic Framework for Surrogate-based Motion Estimation in Radiation Therapy: Concept and First Evaluation. - René Werner; Jan Ehrhardt; Alexander Schmidt-Richberg; Matthias Wilms; Maximilian Blendowski; Heinz Handels (GI-Jahrestagung, 2012)

Modellbasierte 4D-Segmentierung von Lungen mit großen Tumoren in räumlichzeitlichen CT-Bildfolgen. - Matthias Wilms; Jan Ehrhardt; Heinz Handels (GI-Jahrestagung, 2012)

A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors. - Matthias Wilms; Jan Ehrhardt; Heinz Handels (MICCAI (2), 2012)

Pulmonary lobe segmentation with level sets. - Alexander Schmidt-Richberg; Jan Ehrhardt; Matthias Wilms; René Werner; Heinz Handels (Image Processing, 2012)

Simulation und Evaluation tiefenbildgebender Verfahren zur Prädiktion atmungsbedingter Organ- und Tumorbewegungen. - Maximilian Blendowski; Matthias Wilms; René Werner; Heinz Handels (Bildverarbeitung für die Medizin, 2013)

Surrogate-based diffeomorphic motion estimation for radiation therapy: comparison of multivariate regression approaches. - Matthias Wilms; René Werner; Jan Ehrhardt; Alexander Schmidt-Richberg; Maximilian Blendowski; Heinz Handels (Image Processing, 2013)

Segmentierung von ischämischen Schlaganfall-Läsionen in multispektralen MR-Bildern mit Random Decision Forests. - Oskar Maier; Matthias Wilms; Janina von der Gablentz; Ulrike Krämer; Heinz Handels (Bildverarbeitung für die Medizin, 2014)

Kombination von Atemsignalen zur Optimierung der Prädiktion komplexer atmungsbedingter Organ- und Tumorbewegungen. - Jonas Ortmüller; Matthias Wilms; René Werner; Heinz Handels (Bildverarbeitung für die Medizin, 2014)

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers. - Oskar Maier; Matthias Wilms; Janina von der Gablentz; Ulrike Krämer; Heinz Handels (Computer-Aided Diagnosis, 2014)

Statistical analysis of surrogate signals to incorporate respiratory motion variability into radiotherapy treatment planning. - Matthias Wilms; Jan Ehrhardt; René Werner; Mirko Marx; Heinz Handels (Image-Guided Procedures, 2014)

Modellbasierte Simulation der Atembewegung für das Virtual-Reality-Training von Punktionseingriffen. - Matthias Wilms; Dirk Fortmeier; André Mastmeyer; Heinz Handels (Bildverarbeitung für die Medizin, 2015)

Estimation of corresponding locations in ipsilateral mammograms: a comparison of different methods. - Matthias Wilms; Julia Krüger; Mirko Marx; Jan Ehrhardt; Arpad Bischof; Heinz Handels (Computer-Aided Diagnosis, 2015)

Automatic Detection of Cardiac Remodeling Using Global and Local Clinical Measures and Random Forest Classification. - Jan Ehrhardt; Matthias Wilms; Heinz Handels; Dennis Säring (STACOM@MICCAI, 2015)

Multi-atlas Segmentation Using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression. - Mattias P. Heinrich; Matthias Wilms; Heinz Handels (Patch-MI@MICCAI, 2015)

Image Features for Brain Lesion Segmentation Using Random Forests. - Oskar Maier; Matthias Wilms; Heinz Handels (Brainles@MICCAI, 2015)

Direct Visuo-Haptic 4D Volume Rendering Using Respiratory Motion Models. - Dirk Fortmeier; Matthias Wilms; André Mastmeyer; Heinz Handels (IEEE Trans. Haptics, 2015)

Patch-Based Low-Rank Matrix Completion for Learning of Shape and Motion Models from Few Training Samples. - Jan Ehrhardt; Matthias Wilms; Heinz Handels (ECCV (4), 2016)

Model-Based Regularisation for Respiratory Motion Estimation with Sparse Features in Image-Guided Interventions. - Matthias Wilms; In Young Ha; Heinz Handels; Mattias Paul Heinrich (MICCAI (3), 2016)

Real-Time Ultrasound Simulation for Training of US-Guided Needle Insertion in Breathing Virtual Patients. - André Mastmeyer; Matthias Wilms; Dirk Fortmeier; Julian Schröder; Heinz Handels (MMVR, 2016)

Beyond cost function masking: RPCA-based non-linear registration in the context of VLSM. - René Werner; Matthias Wilms; Bastian Cheng; Nils Daniel Forkert (PRNI, 2016)

Model-Based 4D Segmentation of Cardiac Structures in Cine MRI Sequences. - Nassim Bouteldja; Matthias Wilms; Heinz Handels; Dennis Säring; Jan Ehrhardt (Bildverarbeitung für die Medizin, 2017)

Vergleich von Verfahren zur automatischen Detektion der Position und Orientierung des Herzens in 4D-Cine-MRT-Bilddaten. - Marja Fleitmann; Ole Käferlein; Matthias Wilms; Dennis Säring; Heinz Handels; Jan Ehrhardt (Bildverarbeitung für die Medizin, 2017)

Multi-Object Segmentation in Chest X-Ray Using Cascaded Regression Ferns. - In Young Ha; Matthias Wilms; Mattias P. Heinrich (Bildverarbeitung für die Medizin, 2017)

Interpatientenübertragung von Atemmodellen für das Virtual-Reality-Training von Punktionseingriffen. - André Mastmeyer; Matthias Wilms; Heinz Handels (Bildverarbeitung für die Medizin, 2017)

Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study. - René Werner; Daniel Schetelig; Thilo Sothmann; Eike Mücke; Matthias Wilms; Bastian Cheng; Nils Daniel Forkert (Bildverarbeitung für die Medizin, 2017)

Abstract: Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion. - Matthias Wilms; Heinz Handels; Jan Ehrhardt (Bildverarbeitung für die Medizin, 2017)

Abstract: Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations. - Matthias Wilms; Heinz Handels; Jan Ehrhardt (Bildverarbeitung für die Medizin, 2017)

Training CNNs for Image Registration from Few Samples with Model-based Data Augmentation. - Hristina Uzunova; Matthias Wilms; Heinz Handels; Jan Ehrhardt (MICCAI (1), 2017)

Representative Patch-based Active Appearance Models Generated from Small Training Populations. - Matthias Wilms; Heinz Handels; Jan Ehrhardt (MICCAI (1), 2017)

Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures. - André Mastmeyer; Matthias Wilms; Heinz Handels (J. WSCG, 2017)

Multi-resolution multi-object statistical shape models based on the locality assumption. - Matthias Wilms; Heinz Handels; Jan Ehrhardt (Medical Image Anal., 2017)

Abstract: Populationsbasierte 4D Bewegungsatlanten für VR Simulationen. - André Mastmeyer; Matthias Wilms; Heinz Handels (Bildverarbeitung für die Medizin, 2018)

Population-based respiratory 4D motion atlas construction and its application for VR simulations of liver punctures. - André Mastmeyer; Matthias Wilms; Heinz Handels (Image Processing, 2018)

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge. - Avan Suinesiaputra; Pierre Ablin; Xènia Albà; Martino Alessandrini; Jack Allen; Wenjia Bai; Serkan Çimen; Peter Claes; Brett R. Cowan; Jan D'hooge; Nicolas Duchateau; Jan Ehrhardt; et al. (IEEE J. Biomed. Health Informatics, 2018)

Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions. - In Young Ha; Matthias Wilms; Heinz Handels; Mattias P. Heinrich (IEEE Trans. Biomed. Eng., 2019)

Quantitative Comparison of Generative Shape Models for Medical Images. - Hristina Uzunova; Paul Kaftan; Matthias Wilms; Nils D. Forkert; Heinz Handels; Jan Ehrhardt (Bildverarbeitung für die Medizin, 2020)

Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. - Matthias Wilms; Jordan J. Bannister; Pauline Mouches; M. Ethan MacDonald; Deepthi Rajashekar; Sönke Langner; Nils D. Forkert (MLCN/RNO-AI@MICCAI, 2020)

A Kernelized Multi-level Localization Method for Flexible Shape Modeling with Few Training Data. - Matthias Wilms; Jan Ehrhardt; Nils D. Forkert (MICCAI (4), 2020)

Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images. - Jordan J. Bannister; Sebastian Crites; J. David Aponte; David C. Katz; Matthias Wilms; Ophir D. Klein; Francois P. J. Bernier; Richard A. Spritz; Benedikt Hallgrímsson; Nils D. Forkert (Sensors, 2020)

Semantically Guided Large Deformation Estimation with Deep Networks. - In Young Ha; Matthias Wilms; Mattias P. Heinrich (Sensors, 2020)

Analysis of Generative Shape Modeling Approaches - Latent Space Properties and Interpretability. - Hristina Uzunova; Jesse Kruse; Paul Kaftan; Matthias Wilms; Nils D. Forkert; Heinz Handels; Jan Ehrhardt (Bildverarbeitung für die Medizin, 2021)

Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. - Raissa Souza; Anup Tuladhar; Pauline Mouches; Matthias Wilms; Lakshay Tyagi; Nils D. Forkert (BrainLes@MICCAI (2), 2021)

Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows. - Matthias Wilms; Pauline Mouches; Jordan J. Bannister; Deepthi Rajashekar; Sönke Langner; Nils D. Forkert (MLCN@MICCAI, 2021)

Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks. - Kimberly Amador; Matthias Wilms; Anthony J. Winder; Jens Fiehler; Nils D. Forkert (MIDL, 2021)

Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder. - Pauline Mouches; Matthias Wilms; Deepthi Rajashekar; Sönke Langner; Nils Daniel Forkert (MIDL, 2021)

An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks. - Nagesh Subbanna; Matthias Wilms; Anup Tuladhar; Nils D. Forkert (Sensors, 2021)

A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies. - Gabrielle Dagasso; Matthias Wilms; Nils D. Forkert (BIBM, 2022)

A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder. - Emma A. M. Stanley; Deepthi Rajashekar; Pauline Mouches; Matthias Wilms; Kira Plettl; Nils D. Forkert (Computer-Aided Diagnosis, 2022)

Hybrid Spatio-Temporal Transformer Network for Predicting Ischemic Stroke Lesion Outcomes from 4D CT Perfusion Imaging. - Kimberly Amador; Anthony J. Winder; Jens Fiehler; Matthias Wilms; Nils D. Forkert (MICCAI (3), 2022)

Disproportionate Subgroup Impacts and Other Challenges of Fairness in Artificial Intelligence for Medical Image Analysis. - Emma A. M. Stanley; Matthias Wilms; Nils D. Forkert (EPIMI/ML-CDS@MICCAI, 2022)

Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model. - Matthias Wilms; Pauline Mouches; Jordan J. Bannister; Sönke Langner; Nils D. Forkert (MAD@MICCAI, 2022)

Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models. - Jordan J. Bannister; Matthias Wilms; J. David Aponte; David C. Katz; Ophir D. Klein; Francois P. J. Bernier; Richard A. Spritz; Benedikt Hallgrímsson; Nils D. Forkert (Artif. Intell. Medicine, 2022)

A systematic comparison of generative models for medical images. - Hristina Uzunova; Matthias Wilms; Nils D. Forkert; Heinz Handels; Jan Ehrhardt (Int. J. Comput. Assist. Radiol. Surg., 2022)

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. - Raissa Souza; Pauline Mouches; Matthias Wilms; Anup Tuladhar; Sönke Langner; Nils D. Forkert (J. Am. Medical Informatics Assoc., 2022)

Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks. - Kimberly Amador; Matthias Wilms; Anthony J. Winder; Jens Fiehler; Nils D. Forkert (Medical Image Anal., 2022)

Localized Statistical Shape Models for Large-Scale Problems With Few Training Data. - Matthias Wilms; Jan Ehrhardt; Nils D. Forkert (IEEE Trans. Biomed. Eng., 2022)

A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis. - Jordan J. Bannister; Matthias Wilms; J. David Aponte; David C. Katz; Ophir D. Klein; Francois P. J. Bernier; Richard A. Spritz; Benedikt Hallgrímsson; Nils D. Forkert (IEEE J. Biomed. Health Informatics, 2022)

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. - Matthias Wilms; Jordan J. Bannister; Pauline Mouches; M. Ethan MacDonald; Deepthi Rajashekar; Sönke Langner; Nils D. Forkert (IEEE Trans. Medical Imaging, 2022)

An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. - Raissa Souza; Emma A. M. Stanley; Milton Camacho; Matthias Wilms; Nils D. Forkert (Computer-Aided Diagnosis, 2023)

A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis. - Emma A. M. Stanley; Matthias Wilms; Nils D. Forkert (MICCAI (2), 2023)

Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference. - Vibujithan Vigneshwaran; Matthias Wilms; Milton Ivan Camacho; Raissa Souza; Nils D. Forkert (MIDL, 2023)

Towards objective and systematic evaluation of bias in medical imaging AI. - Emma A. M. Stanley; Raissa Souza; Anthony J. Winder; Vedant Gulve; Kimberly Amador; Matthias Wilms; Nils D. Forkert (CoRR, 2023)

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. - Jasmine A. Moore; Anup Tuladhar; Zahinoor Ismail; Pauline Mouches; Matthias Wilms; Nils D. Forkert (Neuroinformatics, 2023)

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system. - Jasmine A. Moore; Matthias Wilms; Alejandro Gutierrez; Zahinoor Ismail; Kayson Fakhar; Fatemeh Hadaeghi; Claus C. Hilgetag; Nils D. Forkert (Frontiers Comput. Neurosci., 2023)

Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. - Alejandro Gutierrez; Anup Tuladhar; Matthias Wilms; Deepthi Rajashekar; Michael D. Hill; Andrew Demchuk; Mayank Goyal; Jens Fiehler; Nils D. Forkert (Int. J. Comput. Assist. Radiol. Surg., 2023)

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. - Raissa Souza; Matthias Wilms; Milton Camacho; G. Bruce Pike; Richard Camicioli; Oury Monchi; Nils D. Forkert (J. Am. Medical Informatics Assoc., 2023)

Unveiling the Temporal Patterns of a 4D CTP Stroke Lesion Outcome Prediction Model Through Attention Analysis. - Kimberly Amador; Anthony J. Winder; Noah Pinel; Jens Fiehler; Matthias Wilms; Nils D. Forkert (ISBI, 2024)

A Novel Multimodal Deep Learning Fusion Framework for Predicting Neovascular Activity Evolution in Exudative Age-Related Macular Degeneration. - Christopher Nielsen; Ahmad O. Ahsan; Matthias Wilms; Nils D. Forkert (DIAMOND/MARIO@MICCAI, 2024)

Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? - Raissa Souza; Emma A. M. Stanley; Richard Camicioli; Oury Monchi; Zahinoor Ismail; Matthias Wilms; Nils D. Forkert (FAIMI/EPIMI@MICCAI, 2024)

Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis. - Emma A. M. Stanley; Raissa Souza; Anthony J. Winder; Matthias Wilms; G. Bruce Pike; Gabrielle Dagasso; Christopher Nielsen; Sarah J. MacEachern; Nils D. Forkert (FAIMI/EPIMI@MICCAI, 2024)

A Multi-modal Deep Learning Framework for Final Infarct Prediction in Acute Ischemic Stroke: Combining CTA, NCCT, and Clinical Data. - Eneko Uruñuela; Annabella Bregazzi; Matthias Wilms (SWITCH/ISLES@MICCAI, 2024)

A Lightweight 3D Conditional Diffusion Model for Self-explainable Brain Age Prediction in Adults and Children. - Matthias Wilms; Ahmad O. Ahsan; Erik Y. Ohara; Gabrielle Dagasso; Elizabeth Macavoy; Emma A. M. Stanley; Vibujithan Vigneshwaran; Nils D. Forkert (MLCN@MICCAI, 2024)

Annotation-free prediction of treatment-specific tissue outcome from 4D CT perfusion imaging in acute ischemic stroke. - Alejandro Gutierrez; Kimberly Amador; Anthony J. Winder; Matthias Wilms; Jens Fiehler; Nils D. Forkert (Comput. Medical Imaging Graph., 2024)

Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems. - Farzaneh Dehghani; Mahsa Dibaji; Fahim Anzum; Lily Dey; Alican Basdemir; Sayeh Bayat; Jean-Christophe Boucher; Steve Drew; Sarah Elaine Eaton; Richard Frayne; Gouri Ginde; Ashley D. Harris; et al. (CoRR, 2024)

MACAW: A Causal Generative Model for Medical Imaging. - Vibujithan Vigneshwaran; Erik Y. Ohara; Matthias Wilms; Nils Daniel Forkert (CoRR, 2024)

Application of a localized morphometrics approach to imaging-derived brain phenotypes for genotype-phenotype associations in pediatric mental health and neurodevelopmental disorders. - Gabrielle Dagasso; Matthias Wilms; Sarah J. MacEachern; Nils D. Forkert (Frontiers Big Data, 2024)

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. - Raissa Souza; Emma A. M. Stanley; Milton Camacho; Richard Camicioli; Oury Monchi; Zahinoor Ismail; Matthias Wilms; Nils D. Forkert (Frontiers Artif. Intell., 2024)

Foundation model-driven distributed learning for enhanced retinal age prediction. - Christopher Nielsen; Raissa Souza; Matthias Wilms; Nils D. Forkert (J. Am. Medical Informatics Assoc., 2024)

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging. - Emma A. M. Stanley; Raissa Souza; Anthony J. Winder; Vedant Gulve; Kimberly Amador; Matthias Wilms; Nils D. Forkert (J. Am. Medical Informatics Assoc., 2024)

Providing clinical context to the spatio-temporal analysis of 4D CT perfusion to predict acute ischemic stroke lesion outcomes. - Kimberly Amador; Alejandro Gutierrez; Anthony J. Winder; Jens Fiehler; Matthias Wilms; Nils D. Forkert (J. Biomed. Informatics, 2024)

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier? - Raissa Souza; Anthony J. Winder; Emma A. M. Stanley; Vibujithan Vigneshwaran; Milton Camacho; Richard Camicioli; Oury Monchi; Matthias Wilms; Nils D. Forkert (IEEE J. Biomed. Health Informatics, 2024)

Accounting for population structure in deep learning models for genomic analysis. - Gabrielle Dagasso; Matthias Wilms; Raissa Souza; Nils D. Forkert (J. Biomed. Informatics, 2025)

A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata. - Kimberly Amador; Noah Pinel; Anthony J. Winder; Jens Fiehler; Matthias Wilms; Nils D. Forkert (Medical Image Anal., 2025)

Structural network measures reveal the emergence of heavy-tailed degree distributions in lottery ticket multilayer perceptrons. - Chris Kang; Jasmine A. Moore; Samuel Robertson; Matthias Wilms; Emma K. Towlson; Nils D. Forkert (Neural Networks, 2025)

Self-supervised identification and elimination of harmful datasets in distributed machine learning for medical image analysis. - Raissa Souza; Emma A. M. Stanley; Anthony J. Winder; Chris Kang; Kimberly Amador; Erik Y. Ohara; Gabrielle Dagasso; Richard Camicioli; Oury Monchi; Zahinoor Ismail; Matthias Wilms; Nils D. Forkert (npj Digit. Medicine, 2025)

Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems. - Farzaneh Dehghani; Mahsa Dibaji; Fahim Anzum; Lily Dey; Alican Basdemir; Sayeh Bayat; Jean-Christophe Boucher; Steve Drew; Sarah Elaine Eaton; Richard Frayne; Gouri Ginde; Ashley D. Harris; et al. (Trans. Mach. Learn. Res., 2025)

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO AMD Progression Challenge. - Rachid Zeghlache; Ikram Brahim; Pierre-Henri Conze; Mathieu Lamard; Mohammed El Amine Lazouni; Elaouaber Zineb Aziza; Leila Ryma Lazouni; Christopher Nielsen; Ahmad O. Ahsan; Matthias Wilms; Nils D. Forkert; Lovre Antonio Budimir; et al. (CoRR, 2025)