Ms. Emma A. M. Stanley Profile

Emma A. M. Stanley

at Univ of Calgary

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Publications (4)

SPIE Journal Paper | 20 September 2024 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 | 7 April 2023 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

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

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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