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2020 – today
- 2024
[j23]Alejandro Gutierrez
, Kimberly Amador, Anthony J. Winder, Matthias Wilms, Jens Fiehler, Nils D. Forkert:
Annotation-free prediction of treatment-specific tissue outcome from 4D CT perfusion imaging in acute ischemic stroke. Comput. Medical Imaging Graph. 114: 102376 (2024)- [j22]Raissa Souza, Emma A. M. Stanley, Milton Camacho, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert:
A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm. Frontiers Artif. Intell. 7 (2024) - [j21]Kimberly Amador, Alejandro Gutierrez, Anthony J. Winder, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
Providing clinical context to the spatio-temporal analysis of 4D CT perfusion to predict acute ischemic stroke lesion outcomes. J. Biomed. Informatics 149: 104567 (2024) - [j20]Raissa Souza, Anthony J. Winder, Emma A. M. Stanley, Vibujithan Vigneshwaran, Milton Camacho, Richard Camicioli, Oury Monchi, Matthias Wilms, Nils D. Forkert:
Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier? IEEE J. Biomed. Health Informatics 28(4): 2047-2054 (2024) - [c51]Kimberly Amador, Anthony J. Winder, Noah Pinel, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
Unveiling the Temporal Patterns of a 4D CTP Stroke Lesion Outcome Prediction Model Through Attention Analysis. ISBI 2024: 1-5 - [c50]Raissa Souza, Emma A. M. Stanley, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert:
Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? FAIMI/EPIMI@MICCAI 2024: 119-128 - [c49]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Matthias Wilms, G. Bruce Pike, Gabrielle Dagasso, Christopher Nielsen, Sarah J. MacEachern, Nils D. Forkert:
Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis. FAIMI/EPIMI@MICCAI 2024: 163-175 - [i5]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 Harris, Yani Ioannou, Catherine Lebel, John Lysack, Leslie Salgado Arzuaga, Emma Stanley, Roberto Souza, Ronnie Souza, Lana Wells, Tyler Williamson, Matthias Wilms, Zaman Wahid, Mark Ungrin, Marina Gavrilova, Mariana Bento:
Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems. CoRR abs/2408.15550 (2024) - 2023
- [j19]Alejandro Gutierrez, Anup Tuladhar, Matthias Wilms, Deepthi Rajashekar, Michael D. Hill, Andrew Demchuk, Mayank Goyal, Jens Fiehler, Nils D. Forkert:
Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. Int. J. Comput. Assist. Radiol. Surg. 18(5): 827-836 (2023) - [j18]Jasmine A. Moore, Matthias Wilms, Alejandro Gutierrez, Zahinoor Ismail, Kayson Fakhar, Fatemeh Hadaeghi, Claus C. Hilgetag, Nils D. Forkert:
Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system. Frontiers Comput. Neurosci. 17 (2023) - [j17]Raissa Souza, Matthias Wilms, Milton Camacho, G. Bruce Pike, Richard Camicioli, Oury Monchi, Nils D. Forkert:
Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J. Am. Medical Informatics Assoc. 30(12): 1925-1933 (2023) - [j16]Jasmine A. Moore, Anup Tuladhar, Zahinoor Ismail, Pauline Mouches, Matthias Wilms, Nils D. Forkert:
Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System. Neuroinformatics 21(1): 45-55 (2023) - [c48]Raissa Souza, Emma A. M. Stanley, Milton Camacho, Matthias Wilms, Nils D. Forkert:
An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. Medical Imaging: Computer-Aided Diagnosis 2023 - [c47]Emma A. M. Stanley, Matthias Wilms, Nils D. Forkert:
A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis. MICCAI (2) 2023: 489-499 - [c46]Vibujithan Vigneshwaran, Matthias Wilms, Milton Ivan Camacho, Raissa Souza, Nils D. Forkert:
Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inference. MIDL 2023: 1304-1317 - [i4]Emma A. M. Stanley, Raissa Souza, Anthony J. Winder, Vedant Gulve, Kimberly Amador, Matthias Wilms, Nils D. Forkert:
Towards objective and systematic evaluation of bias in medical imaging AI. CoRR abs/2311.02115 (2023) - 2022
- [j15]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:
Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models. Artif. Intell. Medicine 134: 102425 (2022) - [j14]Hristina Uzunova, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
A systematic comparison of generative models for medical images. Int. J. Comput. Assist. Radiol. Surg. 17(7): 1213-1224 (2022) - [j13]Raissa Souza, Pauline Mouches, Matthias Wilms, Anup Tuladhar, Sönke Langner, Nils D. Forkert:
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction. J. Am. Medical Informatics Assoc. 30(1): 112-119 (2022) - [j12]Kimberly Amador, Matthias Wilms, Anthony J. Winder, Jens Fiehler, Nils D. Forkert:
Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks. Medical Image Anal. 82: 102610 (2022) - [j11]Matthias Wilms, Jan Ehrhardt, Nils D. Forkert:
Localized Statistical Shape Models for Large-Scale Problems With Few Training Data. IEEE Trans. Biomed. Eng. 69(9): 2947-2957 (2022) - [j10]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:
A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis. IEEE J. Biomed. Health Informatics 26(7): 3229-3239 (2022) - [j9]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE Trans. Medical Imaging 41(9): 2331-2347 (2022) - [c45]Gabrielle Dagasso, Matthias Wilms, Nils D. Forkert:
A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies. BIBM 2022: 3622-3628 - [c44]Emma A. M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert:
A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder. Medical Imaging: Computer-Aided Diagnosis 2022 - [c43]Emma A. M. Stanley, Matthias Wilms, Nils D. Forkert:
Disproportionate Subgroup Impacts and Other Challenges of Fairness in Artificial Intelligence for Medical Image Analysis. EPIMI/ML-CDS@MICCAI 2022: 14-25 - [c42]Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Sönke Langner, Nils D. Forkert:
Disentangling Factors of Morphological Variation in an Invertible Brain Aging Model. MAD@MICCAI 2022: 95-107 - [c41]Kimberly Amador, Anthony J. Winder, Jens Fiehler, Matthias Wilms, Nils D. Forkert:
Hybrid Spatio-Temporal Transformer Network for Predicting Ischemic Stroke Lesion Outcomes from 4D CT Perfusion Imaging. MICCAI (3) 2022: 644-654 - 2021
- [j8]Nagesh Subbanna, Matthias Wilms, Anup Tuladhar, Nils D. Forkert:
An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks. Sensors 21(11): 3874 (2021) - [c40]Hristina Uzunova, Jesse Kruse, Paul Kaftan, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
Analysis of Generative Shape Modeling Approaches - Latent Space Properties and Interpretability. Bildverarbeitung für die Medizin 2021: 344-349 - [c39]Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert:
Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. BrainLes@MICCAI (2) 2021: 420-432 - [c38]Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows. MLCN@MICCAI 2021: 23-33 - [c37]Kimberly Amador, Matthias Wilms, Anthony J. Winder, Jens Fiehler, Nils D. Forkert:
Stroke Lesion Outcome Prediction Based on 4D CT Perfusion Data Using Temporal Convolutional Networks. MIDL 2021: 22-33 - [c36]Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sönke Langner, Nils Daniel Forkert:
Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder. MIDL 2021: 497-506 - 2020
- [j7]In Young Ha, Matthias Wilms, Mattias P. Heinrich:
Semantically Guided Large Deformation Estimation with Deep Networks. Sensors 20(5): 1392 (2020) - [j6]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:
Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images. Sensors 20(11): 3171 (2020) - [c35]Hristina Uzunova, Paul Kaftan, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
Quantitative Comparison of Generative Shape Models for Medical Images. Bildverarbeitung für die Medizin 2020: 201-207 - [c34]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. MLCN/RNO-AI@MICCAI 2020: 23-33 - [c33]Matthias Wilms, Jan Ehrhardt, Nils D. Forkert:
A Kernelized Multi-level Localization Method for Flexible Shape Modeling with Few Training Data. MICCAI (4) 2020: 765-775 - [i3]Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows. CoRR abs/2011.13484 (2020)
2010 – 2019
- 2019
- [j5]In Young Ha, Matthias Wilms, Heinz Handels, Mattias P. Heinrich:
Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions. IEEE Trans. Biomed. Eng. 66(2): 302-310 (2019) - 2018
- [j4]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, Alejandro F. Frangi, Ali Gooya, Vicente Grau, Karim Lekadir, Allen Lu, Anirban Mukhopadhyay, Ilkay Öksüz, Nripesh Parajuli, Xavier Pennec, Marco Pereañez, Catarina Pinto, Paolo Piras, Marc-Michel Rohé, Daniel Rueckert, Dennis Säring, Maxime Sermesant, Kaleem Siddiqi, Mahdi Tabassian, Luciano Teresi, Sotirios A. Tsaftaris, Matthias Wilms, Alistair A. Young, Xingyu Zhang, Pau Medrano-Gracia:
Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge. IEEE J. Biomed. Health Informatics 22(2): 503-515 (2018) - [c32]André Mastmeyer, Matthias Wilms, Heinz Handels:
Abstract: Populationsbasierte 4D Bewegungsatlanten für VR Simulationen. Bildverarbeitung für die Medizin 2018: 200 - [c31]André Mastmeyer, Matthias Wilms, Heinz Handels:
Population-based respiratory 4D motion atlas construction and its application for VR simulations of liver punctures. Medical Imaging: Image Processing 2018: 1057417 - 2017
- [j3]André Mastmeyer, Matthias Wilms, Heinz Handels:
Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures. J. WSCG 25(1): 1-10 (2017) - [j2]Matthias Wilms, Heinz Handels, Jan Ehrhardt:
Multi-resolution multi-object statistical shape models based on the locality assumption. Medical Image Anal. 38: 17-29 (2017) - [c30]Nassim Bouteldja, Matthias Wilms, Heinz Handels, Dennis Säring, Jan Ehrhardt:
Model-Based 4D Segmentation of Cardiac Structures in Cine MRI Sequences. Bildverarbeitung für die Medizin 2017: 18-23 - [c29]René Werner, Daniel Schetelig, Thilo Sothmann, Eike Mücke, Matthias Wilms, Bastian Cheng, Nils Daniel Forkert:
Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study. Bildverarbeitung für die Medizin 2017: 161-166 - [c28]Matthias Wilms, Heinz Handels, Jan Ehrhardt:
Abstract: Patch-Based Learning of Shape, Appearance, and Motion Models from Few Training Samples by Low-Rank Matrix Completion. Bildverarbeitung für die Medizin 2017: 215-216 - [c27]In Young Ha, Matthias Wilms, Mattias P. Heinrich:
Multi-Object Segmentation in Chest X-Ray Using Cascaded Regression Ferns. Bildverarbeitung für die Medizin 2017: 254-259 - [c26]Marja Fleitmann, Ole Käferlein, Matthias Wilms, Dennis Säring, Heinz Handels, Jan Ehrhardt:
Vergleich von Verfahren zur automatischen Detektion der Position und Orientierung des Herzens in 4D-Cine-MRT-Bilddaten. Bildverarbeitung für die Medizin 2017: 293-298 - [c25]André Mastmeyer, Matthias Wilms, Heinz Handels:
Interpatientenübertragung von Atemmodellen für das Virtual-Reality-Training von Punktionseingriffen. Bildverarbeitung für die Medizin 2017: 340-345 - [c24]Matthias Wilms, Heinz Handels, Jan Ehrhardt:
Abstract: Learning of Representative Multi-Resolution Multi-Object Statistical Shape Models from Small Training Populations. Bildverarbeitung für die Medizin 2017: 359-360 - [c23]Matthias Wilms, Heinz Handels, Jan Ehrhardt:
Representative Patch-based Active Appearance Models Generated from Small Training Populations. MICCAI (1) 2017: 152-160 - [c22]Hristina Uzunova, Matthias Wilms, Heinz Handels, Jan Ehrhardt:
Training CNNs for Image Registration from Few Samples with Model-based Data Augmentation. MICCAI (1) 2017: 223-231 - [i2]André Mastmeyer, Matthias Wilms, Heinz Handels:
Interpatient Respiratory Motion Model Transfer for Virtual Reality Simulations of Liver Punctures. CoRR abs/1707.08554 (2017) - [i1]André Mastmeyer, Matthias Wilms, Heinz Handels:
Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures. CoRR abs/1712.01893 (2017) - 2016
- [c21]Jan Ehrhardt, Matthias Wilms, Heinz Handels:
Patch-Based Low-Rank Matrix Completion for Learning of Shape and Motion Models from Few Training Samples. ECCV (4) 2016: 712-727 - [c20]Matthias Wilms, In Young Ha, Heinz Handels, Mattias Paul Heinrich:
Model-Based Regularisation for Respiratory Motion Estimation with Sparse Features in Image-Guided Interventions. MICCAI (3) 2016: 89-97 - [c19]André Mastmeyer, Matthias Wilms, Dirk Fortmeier, Julian Schröder, Heinz Handels:
Real-Time Ultrasound Simulation for Training of US-Guided Needle Insertion in Breathing Virtual Patients. MMVR 2016: 219-226 - [c18]René Werner, Matthias Wilms, Bastian Cheng, Nils Daniel Forkert:
Beyond cost function masking: RPCA-based non-linear registration in the context of VLSM. PRNI 2016: 1-4 - 2015
- [j1]Dirk Fortmeier, Matthias Wilms, André Mastmeyer, Heinz Handels:
Direct Visuo-Haptic 4D Volume Rendering Using Respiratory Motion Models. IEEE Trans. Haptics 8(4): 371-383 (2015) - [c17]Matthias Wilms, Dirk Fortmeier, André Mastmeyer, Heinz Handels:
Modellbasierte Simulation der Atembewegung für das Virtual-Reality-Training von Punktionseingriffen. Bildverarbeitung für die Medizin 2015: 317-322 - [c16]Matthias Wilms, Julia Krüger, Mirko Marx, Jan Ehrhardt, Arpad Bischof, Heinz Handels:
Estimation of corresponding locations in ipsilateral mammograms: a comparison of different methods. Medical Imaging: Computer-Aided Diagnosis 2015: 94142B - [c15]Oskar Maier, Matthias Wilms, Heinz Handels:
Image Features for Brain Lesion Segmentation Using Random Forests. Brainles@MICCAI 2015: 119-130 - [c14]Mattias P. Heinrich, Matthias Wilms, Heinz Handels:
Multi-atlas Segmentation Using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression. Patch-MI@MICCAI 2015: 146-153 - [c13]Jan Ehrhardt, Matthias Wilms, Heinz Handels, Dennis Säring:
Automatic Detection of Cardiac Remodeling Using Global and Local Clinical Measures and Random Forest Classification. STACOM@MICCAI 2015: 199-207 - 2014
- [c12]Jonas Ortmüller, Matthias Wilms, René Werner, Heinz Handels:
Kombination von Atemsignalen zur Optimierung der Prädiktion komplexer atmungsbedingter Organ- und Tumorbewegungen. Bildverarbeitung für die Medizin 2014: 72-77 - [c11]Oskar Maier, Matthias Wilms, Janina von der Gablentz, Ulrike Krämer, Heinz Handels:
Segmentierung von ischämischen Schlaganfall-Läsionen in multispektralen MR-Bildern mit Random Decision Forests. Bildverarbeitung für die Medizin 2014: 156-161 - [c10]Oskar Maier, Matthias Wilms, Janina von der Gablentz, Ulrike Krämer, Heinz Handels:
Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers. Medical Imaging: Computer-Aided Diagnosis 2014: 903504 - [c9]Matthias Wilms, Jan Ehrhardt, René Werner, Mirko Marx, Heinz Handels:
Statistical analysis of surrogate signals to incorporate respiratory motion variability into radiotherapy treatment planning. Medical Imaging: Image-Guided Procedures 2014: 90360J - 2013
- [c8]Maximilian Blendowski, Matthias Wilms, René Werner, Heinz Handels:
Simulation und Evaluation tiefenbildgebender Verfahren zur Prädiktion atmungsbedingter Organ- und Tumorbewegungen. Bildverarbeitung für die Medizin 2013: 350-355 - [c7]Matthias Wilms, René Werner, Jan Ehrhardt, Alexander Schmidt-Richberg, Maximilian Blendowski, Heinz Handels:
Surrogate-based diffeomorphic motion estimation for radiation therapy: comparison of multivariate regression approaches. Medical Imaging: Image Processing 2013: 866915 - 2012
- [c6]Alexander Schmidt-Richberg, Jan Ehrhardt, Matthias Wilms, René Werner, Heinz Handels:
Evaluation of Algorithms for Lung Fissure Segmentation in CT Images. Bildverarbeitung für die Medizin 2012: 201-206 - [c5]Matthias Wilms, Jan Ehrhardt, Heinz Handels:
Modellbasierte 4D-Segmentierung von Lungen mit großen Tumoren in räumlichzeitlichen CT-Bildfolgen. GI-Jahrestagung 2012: 1764-1773 - [c4]René Werner, Jan Ehrhardt, Alexander Schmidt-Richberg, Matthias Wilms, Maximilian Blendowski, Heinz Handels:
A Diffeomorphic Framework for Surrogate-based Motion Estimation in Radiation Therapy: Concept and First Evaluation. GI-Jahrestagung 2012: 1774-1785 - [c3]Matthias Wilms, Jan Ehrhardt, Heinz Handels:
A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors. MICCAI (2) 2012: 347-354 - [c2]Alexander Schmidt-Richberg, Jan Ehrhardt, Matthias Wilms, René Werner, Heinz Handels:
Pulmonary lobe segmentation with level sets. Medical Imaging: Image Processing 2012: 83142V - 2011
- [c1]Matthias Wilms, Jan Ehrhardt, Heinz Handels:
Automatische Segmentierung der Lungenflügel in CT-Daten. Bildverarbeitung für die Medizin 2011: 119-123
Coauthor Index
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