Dr. Matthias Wilms Profile

Dr. Matthias Wilms

at Univ of Calgary

SPIE Involvement:

Conference Program Committee | Author

Publications (15)

SPIE Journal Paper | 20 September 2024 Open Access

Open Access

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson’s disease classification

Raissa Souza, Emma A. Stanley, Vedant Gulve, Jasmine Moore, Chris Kang, Richard Camicioli, Oury Monchi, Zahinoor Ismail, Matthias Wilms, Nils D. Forkert

JMI, Vol. 11, Issue 05, 054502, (September 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.5.054502

KEYWORDS: Scanners, Data modeling, Education and training, Machine learning, Principal component analysis, Diseases and disorders, Neuroimaging, Data acquisition, Image acquisition, Head

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Proceedings Article | 2 April 2024 Presentation + Paper

Presentation + Paper

CNN-derived brain age gaps of different neurological and cardiovascular diseases in the UK Biobank and identifying affected brain regions

Elizabeth Mcavoy, Matthias Wilms, Nils Forkert

Proceedings Volume 12931, 129310A (2024) https://doi.org/10.1117/12.3006650

KEYWORDS: Brain, Cardiovascular disorders, Brain diseases, Mental disorders, Bias correction, Neurological disorders, Image processing, Artificial intelligence, Deep convolutional neural networks, Brain imaging

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Proceedings Article | 2 April 2024 Presentation + Paper

Presentation + Paper

MACAW 3D: A masked causal normalizing flow method for counterfactual 3D brain image generation

Erik Ohara, Finn Vamosi, Harsh Patil, Vibujithan Vigneshwaran, Matthias Wilms, Nils Forkert

Proceedings Volume 12931, 129310K (2024) https://doi.org/10.1117/12.3006482

KEYWORDS: 3D modeling, 3D image processing, Brain, Data modeling, Neuroimaging, Medical imaging, Deep learning, Image processing, Artificial intelligence

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Proceedings Article | 10 April 2023 Presentation + Paper

Presentation + Paper

A pipeline for multivariate genome-wide associations studies with morphological brain features

Gabrielle Dagasso, Matthias Wilms, Nils Forkert

Proceedings Volume 12469, 124690L (2023) https://doi.org/10.1117/12.2654600

KEYWORDS: Brain, Genetics, Brain diseases, Neuroimaging, Mental disorders, Biological imaging, Statistical analysis, Neurological disorders, Medicine

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Proceedings Article | 7 April 2023 Poster + Paper

Poster + Paper

An analysis of intensity harmonization techniques for Parkinson’s multi-site MRI datasets

Raissa Souza, Emma A. Stanley, Milton Camacho, Matthias Wilms, Nils Forkert

Proceedings Volume 12465, 124652B (2023) https://doi.org/10.1117/12.2653948

KEYWORDS: Data modeling, Histograms, Performance modeling, Magnetic resonance imaging, Education and training, Parkinson disease, Neuroimaging, Machine learning, Solid modeling, Scanners

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SPIE Journal Paper | 26 August 2022

Fairness-related performance and explainability effects in deep learning models for brain image analysis

Emma A. Stanley, Matthias Wilms, Pauline Mouches, Nils D. Forkert

JMI, Vol. 9, Issue 06, 061102, (August 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.6.061102

KEYWORDS: Brain, Performance modeling, Brain mapping, Cerebellum, Magnetic resonance imaging, Data modeling, Amygdala, Neuroimaging, Solid modeling, Artificial intelligence

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Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons for unfairness. Thus, the aim of this work was to analyze the effects of previously established sociodemographic-related confounders on classifier performance and explainability methods.Approach: A convolutional neural network (CNN) was trained to predict biological sex from T1-weighted brain MRI datasets of 4547 9- to 10-year-old adolescents from the Adolescent Brain Cognitive Development study. Performance disparities of the trained CNN between White and Black subjects were analyzed and saliency maps were generated for each subgroup at the intersection of sex and race.Results: The classification model demonstrated a significant difference in the percentage of correctly classified White male (90.3 % ± 1.7 % ) and Black male (81.1 % ± 4.5 % ) children. Conversely, slightly higher performance was found for Black female (89.3 % ± 4.8 % ) compared with White female (86.5 % ± 2.0 % ) children. Saliency maps showed subgroup-specific differences, corresponding to brain regions previously associated with pubertal development. In line with this finding, average pubertal development scores of subjects used in this study were significantly different between Black and White females (p < 0.001) and males (p < 0.001).Conclusions: We demonstrate that a CNN with significantly different sex classification performance between Black and White adolescents can identify different important brain regions when comparing subgroup saliency maps. Importance scores vary substantially between subgroups within brain structures associated with pubertal development, a race-associated confounder for predicting sex. We illustrate that unfair models can produce different XAI results between subgroups and that these results may explain potential reasons for biased performance.

Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

A comparative analysis of the impact of data distribution on distributed learning with a traveling model for brain age prediction

Raissa Souza, Agampreet Aulakh, Pauline Mouches, Anup Tuladhar, Matthias Wilms, Sönke Langner, Nils Forkert

Proceedings Volume 12037, 1203702 (2022) https://doi.org/10.1117/12.2612728

KEYWORDS: Data modeling, Brain, Neuroimaging, Performance modeling, Machine learning, Data centers, Magnetic resonance imaging, Solid modeling, Medical research, Feature extraction

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder

Emma A. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils Forkert

Proceedings Volume 12033, 1203315 (2022) https://doi.org/10.1117/12.2607509

KEYWORDS: Brain, Data modeling, Convolutional neural networks, Magnetic resonance imaging, Cognitive modeling, Neuroimaging, Diagnostics, Amygdala, Computer aided diagnosis and therapy, Machine learning

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

Understanding privacy risks in typical deep learning models for medical image analysis

Nagesh Subbanna, Anup Tuladhar, Matthias Wilms, Nils D. Forkert

Proceedings Volume 11601, 116010E (2021) https://doi.org/10.1117/12.2582014

KEYWORDS: Data modeling, Visual process modeling, Medical imaging, Image segmentation, Magnetic resonance imaging, Image restoration, Machine vision, Ischemic stroke, Image compression, Databases

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Deep learning in medical imaging typically requires sensitive and confidential patient data for model training. Recent research in computer vision has shown that it is possible to recover training data from trained models using model inversion techniques. In this paper, we investigate the degree to which encoder-decoder like architectures (U-Nets, etc) commonly used in medical imaging are vulnerable to simple model inversion attacks. Utilising a database consisting of 20 MRI datasets from acute ischemic stroke patients, we trained an autoencoder model for image reconstruction and a U-Net model for lesion segmentation. In the second step, model inversion decoders were developed and trained to reconstruct the original MRIs from the low dimensional representation of the trained autoencoder and the U-Net model. The inversion decoders were trained using 24 independent MRI datasets of acute stroke patients not used for training of the original models. Skull-stripped as well as the full original datasets including the skull and other non-brain tissues were used for model training and evaluation. The results show that the trained inversion decoder can be used to reconstruct training datasets after skull stripping given the latent space of the autoencoder trained for image reconstruction (mean correlation coefficient= 0.49), while it was not possible to fully reconstruct the original image used for training of a segmentation task UNet (mean correlation coefficient=0.18). These results are further supported by the structural similarity index measure (SSIM) scores, which show a mean SSIM score of 0.51± 0.14 for the autoencoder trained for image reconstruction, while the average SSIM score for the U-Net trained for the lesion segmentation task was 0.28±0.12. The same experiments were then conducted on the same images but without skull stripping. In this case, the U-Net trained for segmentation shows significantly worse results, while the autoencoder trained for image reconstruction is not affected. Our results suggest that an autoencoder model trained for image compression can be inverted with high accuracy while this is much harder to achieve for a U-Net trained for lesion segmentation.

Proceedings Article | 2 March 2018 Presentation + Paper

Presentation + Paper

Population-based respiratory 4D motion atlas construction and its application for VR simulations of liver punctures

Andre Mastmeyer, Matthias Wilms, Heinz Handels

Proceedings Volume 10574, 1057417 (2018) https://doi.org/10.1117/12.2293092

KEYWORDS: Motion models, Acquisition tracking and pointing, 3D modeling, Data modeling, Liver, Virtual reality, Lung, Image registration, Spirometry, Data acquisition

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Proceedings Article | 20 March 2015 Paper

Paper

Estimation of corresponding locations in ipsilateral mammograms: a comparison of different methods

Matthias Wilms, Julia Krüger, Mirko Marx, Jan Ehrhardt, Arpad Bischof, Heinz Handels

Proceedings Volume 9414, 94142B (2015) https://doi.org/10.1117/12.2081862

KEYWORDS: Breast, Mammography, Nipple, Chest, 3D modeling, Image compression, Data modeling, Breast cancer, Computer aided diagnosis and therapy, Tissues

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Proceedings Article | 24 March 2014 Paper

Paper

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

Proceedings Volume 9035, 903504 (2014) https://doi.org/10.1117/12.2043494

KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, Ischemic stroke, Tissues, Feature extraction, Binary data, Neuroimaging, Feature selection, Databases

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Proceedings Article | 12 March 2014 Paper

Paper

Statistical analysis of surrogate signals to incorporate respiratory motion variability into radiotherapy treatment planning

Matthias Wilms, Jan Ehrhardt, René Werner, Mirko Marx, Heinz Handels

Proceedings Volume 9036, 90360J (2014) https://doi.org/10.1117/12.2043568

KEYWORDS: Statistical analysis, Motion models, Tumors, Monte Carlo methods, 4D CT imaging, Data modeling, Statistical modeling, Radiotherapy, Spirometry, Motion measurement

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Proceedings Article | 13 March 2013 Paper

Paper

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

Proceedings Volume 8669, 866915 (2013) https://doi.org/10.1117/12.2002428

KEYWORDS: Motion estimation, Principal component analysis, Radiotherapy, Tumors, Lung, Motion models, Motion measurement, 4D CT imaging, Chest, Simulation of CCA and DLA aggregates

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Proceedings Article | 14 February 2012 Paper

Paper

Pulmonary lobe segmentation with level sets

Alexander Schmidt-Richberg, Jan Ehrhardt, Matthias Wilms, René Werner, Heinz Handels

Proceedings Volume 8314, 83142V (2012) https://doi.org/10.1117/12.911378

KEYWORDS: Image segmentation, Lung, Image quality, Image enhancement, Computed tomography, Image processing, Diagnostics, Medical imaging, Silicon, Image registration

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Showing 5 of 15 publications

Conference Committee Involvement (2)

Computer-Aided Diagnosis

17 February 2025 | San Diego, California, United States

Computer-Aided Diagnosis

19 February 2024 | San Diego, California, United States

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