Visual Computing zur Analyse von zerebralen arteriovenösen Malformationen in 3D- und 4D-MR Bilddaten. - Dennis Säring; Jens Fiehler; Nils Daniel Forkert; Milena Piening; Heinz Handels (Bildverarbeitung für die Medizin, 2007)

Automatische Lokalisation und hämodynamische Charakterisierung von Gefäßstrukturen bei arteriovenösen Malformationen. - Nils Daniel Forkert; Dennis Säring; Jens Fiehler; Till Illies; Heinz Handels (Bildverarbeitung für die Medizin, 2008)

Fully Automatic Skull-Stripping in 3D Time-of-Flight MRA Image Sequences. - Nils Daniel Forkert; Dennis Säring; Jens Fiehler; Till Illies; Matthias Färber 002; Dietmar P. F. Möller; Heinz Handels (VCBM, 2008)

Automatische Segmentierung der zerebralen Gefäße aus 3D-TOF-MRA-Bildsequenzen mittels Fuzzy-Methoden. - Nils Daniel Forkert; Dennis Säring; Karolin Wenzel; Jens Fiehler; Till Illies; Dietmar P. F. Möller; Heinz Handels (Bildverarbeitung für die Medizin, 2009)

AnToNIa: A Software Tool for the Hemodynamic Analysis of Cerebral Vascular Malformations Using 3D and 4D MRA Image Sequences. - Nils Daniel Forkert; Dennis Säring; Jens Fiehler; Till Illies; Heinz Handels (GI Jahrestagung, 2009)

Analyse und dynamische 3D-Visualisierung des Blutflusses von zerebralen Gefäßstrukturen unter Verwendung von 3D- und 4D-Magnetresonanzangiographie-Bildfolgen. - Nils Daniel Forkert (Informatiktage, 2009)

Fuzzy-Based Extraction of Vascular Structures from Time-of-Flight MR Images. - Nils Daniel Forkert; Dennis Säring; Karolin Wenzel; Till Illies; Jens Fiehler; Heinz Handels (MIE, 2009)

Analysis and dynamic 3D visualization of cerebral blood flow combining 3D and 4D MR image sequences. - Nils Daniel Forkert; Dennis Säring; Jens Fiehler; Till Illies; Dietmar P. F. Möller; Heinz Handels (Image-Guided Procedures, 2009)

Experimental Assessment of Infarct Lesion Growth in Mice using Time-Resolved T2* MR Image Sequences. - Nils Daniel Forkert; Dennis Säring; Andrea Eisenbeis; Frank Leypoldt; Jens Fiehler; Heinz Handels (Bildverarbeitung für die Medizin, 2010)

Graphen- und Level-Set-basierte Nachverarbeitung von 3D-Gefäßsegmentierungen. - Nils Daniel Forkert; Alexander Schmidt-Richberg; Dennis Säring; Jens Fiehler; Till Illies; Dietmar P. F. Möller; Heinz Handels (Bildverarbeitung für die Medizin, 2010)

Automatic Analysis of the Anatomy of Arteriovenous Malformations using 3D and 4D MRA Image Sequences. - Nils Daniel Forkert; Dennis Säring; Heinz Handels (MedInfo, 2010)

Evaluation of Methods for Bolus Arrival Time Determination using a Four-dimensional MRA Flow Phantom. - Dennis Säring; Nils Daniel Forkert; Till Illies; Jens Fiehler; Heinz Handels (MedInfo, 2010)

Closing of interrupted vascular segmentations: an automatic approach based on shortest paths and level sets. - Nils Daniel Forkert; Alexander Schmidt-Richberg; Dennis Säring; Till Illies; Jens Fiehler; Heinz Handels (Image Processing, 2010)

Vesselness-geführte Level-Set Segmentierung von zerebralen Gefäßen. - Nils Daniel Forkert; Alexander Schmidt-Richberg; Jan Ehrhardt; Jens Fiehler; Heinz Handels; Dennis Säring (Bildverarbeitung für die Medizin, 2011)

Segmentierung rekanalisierter Blutgefäße nach Lysetherapie unter Verwendung von Time-of-Flight MRA Datensätzen. - Tobias Verleger; Dennis Säring; Susanne Siemonsen; Jens Fiehler; Nils Daniel Forkert (GI-Jahrestagung, 2011)

Image-based Classification of Parkinsonian Syndromes Using T2'-Atlases. - Nils Daniel Forkert; Alexander Schmidt-Richberg; Brigitte Holst; Alexander Münchau; Heinz Handels; Kai Boelmans (MIE, 2011)

Direction-dependent level set segmentation of cerebrovascular structures. - Nils Daniel Forkert; Dennis Säring; Till Illies; Jens Fiehler; Jan Ehrhardt; Heinz Handels; Alexander Schmidt-Richberg (Image Processing, 2011)

Automatische atlasbasierte Differenzierung von klassischen und atypischen Parkinsonsyndromen. - Nils Daniel Forkert; Alexander Schmidt-Richberg; Alexander Münchau; Jens Fiehler; Heinz Handels; Kai Boelmans (Bildverarbeitung für die Medizin, 2012)

Automatische Detektion von Aneurysmen in 3D Time-of-Flight Magnetresonanzangiographie Datensätzen. - Santiago Suniaga; René Werner; Andre Kemmling; Michael Groth; Jens Fiehler; Nils Daniel Forkert (GI-Jahrestagung, 2012)

Computer-Aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets. - Santiago Suniaga; René Werner; Andre Kemmling; Michael Groth; Jens Fiehler; Nils Daniel Forkert (MLMI, 2012)

Generation of a Probabilistic Arterial Cerebrovascular Atlas Derived from 700 Time-of-Flight MRA Datasets. - Nils Daniel Forkert; Santiago Suniaga; Jens Fiehler; Heike Wersching; Stefan Knecht; Andre Kemmling (MIE, 2012)

Modellbasierte Analyse zerebrovaskulärer Erkrankungen durch Kombination von 3D und 4D MRA Datensätzen. - Nils Daniel Forkert (Ausgezeichnete Informatikdissertationen, 2013)

Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations. - Nils Daniel Forkert; Till Illies; Einar Goebell; Jens Fiehler; Dennis Säring; Heinz Handels (Int. J. Comput. Assist. Radiol. Surg., 2013)

Model-Based Analysis of Cerebrovascular Diseases Combining 3D and 4D MRA Datasets. - Nils Daniel Forkert (2013)

Automatische Detektion von Okklusionen zerebraler Arterien in 3D-Magnetresonanzangiographiedaten. - Albrecht Kleinfeld; Oskar Maier; Nils Daniel Forkert; Heinz Handels (Bildverarbeitung für die Medizin, 2015)

Effect of sample size on multi-parametric prediction of tissue outcome in acute ischemic stroke using a random forest classifier. - Nils Daniel Forkert; Jens Fiehler (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2015)

Model-based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets. - Nils Daniel Forkert (it Inf. Technol., 2015)

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)

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)

Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain. - Renzo Phellan; Thomas Lindner 002; Alexandre X. Falcão; Nils Daniel Forkert (Computer-Aided Diagnosis, 2017)

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets. - Sahand Talai; Kai Boelmans; Jan Sedlacik; Nils Daniel Forkert (Computer-Aided Diagnosis, 2017)

Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning. - Javier Villafruela; Sebastian Crites; Bastian Cheng; Christian Knaack; Götz Thomalla; Bijoy K. Menon; Nils Daniel Forkert (Computer-Aided Diagnosis, 2017)

Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention. - Anthony J. Winder; Susanne Siemonsen; Fabian Flottmann; Jens Fiehler; Nils Daniel Forkert (Computer-Aided Diagnosis, 2017)

Automatic Full Femur Segmentation from Computed Tomography Datasets Using an Atlas-Based Approach. - Bryce A. Besler; Andrew S. Michalski; Nils Daniel Forkert; Steven K. Boyd (MSKI@MICCAI, 2017)

Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network. - Renzo Phellan; Alan Peixinho; Alexandre X. Falcão; Nils Daniel Forkert (CVII-STENT/LABELS@MICCAI, 2017)

Robust cerebrovascular segmentation in 4D ASL MRA images. - Renzo Phellan; Thomas Lindner 002; Michael Helle; Alexandre X. Falcão; Nils Daniel Forkert (ISBI, 2018)

Bone Adaptation as Level Set Motion. - Bryce A. Besler; Leigh Gabel; Lauren A. Burt; Nils Daniel Forkert; Steven K. Boyd (MSKI@MICCAI, 2018)

Four-Dimensional ASL MR Angiography Phantoms with Noise Learned by Neural Styling. - Renzo Phellan; Thomas Lindner 002; Michael Helle; Thiago Vallin Spina; Alexandre X. Falcão; Nils Daniel Forkert (CVII-STENT/LABELS@MICCAI, 2018)

DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes. - Giles Tetteh; Velizar Efremov; Nils D. Forkert; Matthias Schneider 002; Jan Kirschke; Bruno Weber; Claus Zimmer; Marie Piraud; Bjoern H. Menze (CoRR, 2018)

Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images. - Renzo Phellan; Thomas Lindner 002; Michael Helle; Alexandre X. Falcão; Nils Daniel Forkert (IEEE Trans. Biomed. Eng., 2018)

The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - A Flow Phantom Study. - Renzo Phellan; Thomas Lindner 002; Michael Helle; Alexandre X. Falcão; Nils D. Forkert (MLMECH/CVII-STENT@MICCAI, 2019)

A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms. - Renzo Phellan; Thomas Lindner 002; Michael Helle; Alexandre X. Falcão; Thomas W. Okell; Nils D. Forkert (Medical Image Anal., 2019)

Rapid solution of the Bloch-Torrey equation in anisotropic tissue: Application to dynamic susceptibility contrast MRI of cerebral white matter. - Jonathan Doucette; Luxi Wei; Enedino Hernández-Torres; Christian Kames; Nils D. Forkert; Rasmus Aamand; Torben Ellegaard Lund; Brian Hansen; Alexander Rauscher (NeuroImage, 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)

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks. - Anup Tuladhar; Serena Schimert; Deepthi Rajashekar; Helge C. Kniep; Jens Fiehler; Nils D. Forkert (IEEE Access, 2020)

Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling. - Anup Tuladhar; Sascha Gill; Zahinoor Ismail; Nils D. Forkert; Alzheimer's Disease Neuroimaging Initiative (J. Biomed. Informatics, 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)

Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA. - Renzo Phellan; Thomas Lindner 002; Michael Helle; Alexandre X. Falcão; Clarissa L. Yasuda; Magdalena J. Sokolska; Hans Rolf Jäger; Nils Daniel Forkert (IEEE Trans. Biomed. Eng., 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)

Federated Learning Using Variable Local Training for Brain Tumor Segmentation. - Anup Tuladhar; Lakshay Tyagi; Raissa Souza; 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)

Local Morphometry of Closed, Implicit Surfaces. - Bryce A. Besler; Tannis D. Kemp; Andrew S. Michalski; Nils D. Forkert; Steven K. Boyd (CoRR, 2021)

Constructing High-Order Signed Distance Maps from Computed Tomography Data with Application to Bone Morphometry. - Bryce A. Besler; Tannis D. Kemp; Nils D. Forkert; Steven K. Boyd (CoRR, 2021)

Modeling Neurodegeneration in silico With Deep Learning. - Anup Tuladhar; Jasmine A. Moore; Zahinoor Ismail; Nils D. Forkert (Frontiers Neuroinformatics, 2021)

Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets. - Bryce A. Besler; Andrew S. Michalski; Michael T. Kuczynski; Aleena Abid; Nils D. Forkert; Steven K. Boyd (Medical Image Anal., 2021)

Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol. - Samaneh Nobakht; Morgan Schaeffer; Nils D. Forkert; Sean M. Nestor; Sandra E. Black; Philip A. Barber; Alzheimer's Disease Neuroimaging Initiative (Sensors, 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)

Simulating progressive neurodegeneration in silico with deep artificial neural networks. - Anup Tuladhar; Jasmine A. Moore; Zahinoor Ismail; Nils D. Forkert (CogSci, 2022)

Weakly Supervised Medical Image Segmentation with Soft Labels and Noise Robust Loss. - Banafshe Felfeliyan; Abhilash Rakkunedeth Hareendranathan; Gregor Kuntze; Stephanie Wichuk; Nils D. Forkert; Jacob L. Jaremko; Janet Lenore Ronsky (ICPR Workshops (2), 2022)

Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. - Alejandro Gutierrez; Anup Tuladhar; Deepthi Rajashekar; Nils D. Forkert (Computer-Aided Diagnosis, 2022)

Stroke lesion localization in 3D MRI datasets with deep reinforcement learning. - Samuel Robertson; Anup Tuladhar; Deepthi Rajashekar; Nils D. Forkert (Computer-Aided Diagnosis, 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)

Investigating the Vulnerability of Federated Learning-Based Diabetic Retinopathy Grade Classification to Gradient Inversion Attacks. - Christopher Nielsen; Anup Tuladhar; Nils D. Forkert (OMIA@MICCAI, 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)

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images of Mouse Embryos Using Convolutional Neural Networks. - Lucas Lo Vercio; Rebecca M. Green; Samuel Robertson; Sienna Guo; Andreas Dauter; Marta Marchini; Marta Vidal-García; Xiang Zhao; Anandita Mahika; Ralph S. Marcucio; Benedikt Hallgrímsson; Nils D. Forkert (IEEE Access, 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)

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation. - Banafshe Felfeliyan; Abhilash Rakkunedeth Hareendranathan; Gregor Kuntze; David Cornell; Nils D. Forkert; Jacob L. Jaremko; Janet Lenore Ronsky (CoRR, 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)

Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks. - Anup Tuladhar; Serena Schimert; Deepthi Rajashekar; Helge C. Kniep; Jens Fiehler; Nils D. Forkert (IEEE DataPort, 2022)

On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity. - Raissa Souza; Emma A. M. Stanley; Nils D. Forkert (CLIP/FAIMI/EPIMI@MICCAI, 2023)

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)

Self-supervised-RCNN for medical image segmentation with limited data annotation. - Banafshe Felfeliyan; Nils D. Forkert; Abhilash Rakkunedeth Hareendranathan; David Cornel; Yuyue Zhou; Gregor Kuntze; Jacob L. Jaremko; Janet Lenore Ronsky (Comput. Medical Imaging Graph., 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)

Spatio-Temporal Deep Learning for Final Infarct Prediction Using Acute Stroke CT Perfusion Data. - Kimberly Amador; Anthony J. Winder; Nils D. Forkert (SWITCH/ISLES@MICCAI, 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 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)

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

MOX-NET: Multi-stage deep hybrid feature fusion and selection framework for monkeypox classification. - Sarmad Maqsood; Robertas Damasevicius; Sana Shahid; Nils D. Forkert (Expert Syst. Appl., 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)

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)

Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification. - Emma A. M. Stanley; Raghav Mehta; Mélanie Roschewitz; Nils D. Forkert; Ben Glocker (CoRR, 2025)

Csec-net: a novel deep features fusion and entropy-controlled firefly feature selection framework for leukemia classification. - Sarmad Maqsood; Robertas Damasevicius; Rytis Maskeliunas; Nils D. Forkert; Shahab Haider; Shahid Latif (Health Inf. Sci. Syst., 2025)