Year: 2026
Type: article
Abstract: Despite increasing accuracy of deep-learning-based medical imaging classifiers for detecting complex neurodegenerative disorders, an inherent lack of transparency and interpretability in their decisions poses a challenge to their clinical and research applications. While existing explainability methods provide insights into broader aspects of a classification decision, they do not provide a direct interpretation of how specific image information was used for a classification. Geometric, or morphological, brain changes are known to exist in multiple neurodegenerative disorders, and explaining how these changes are being used by a classifier would provide domain experts with an easily interpretable understanding of morphology contributing towards the classification decision. Our work proposes a counterfactual method to explain morphological features used by deep-learning classifiers for classifying image instances. Furthermore, to provide a more interpretable understanding of common morphological features used by a classifier for the population, we combine the instance explanations to describe average morphological brain changes used by the classifier in MNI152 space. The proposed method was tested using classifiers trained for detecting Parkinson's Disease (PD) and a simulated disease in T1-w MRIs. The counterfactual images generated by our method were shown to explain learnt disease-related image information, as the classification of more than 98% of images for the PD classifier was changed by modifying the explained aspects of the image morphology. By deriving geometric explanations used by deep-learning classifiers, future research can advance trustworthiness by validating the classifiers against existing disease knowledge and potentially harness them to explore novel disease processes. Our code is available at: https://github.com/ankusharudkar/Morphometric-Explanations.
Source: Human Brain Mapping
DOI: 10.1002/hbm.70633
Institutions: Adelaide University, The University of Adelaide
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,520
Funders: Foundation for the National Institutes of Health, Alzheimer's Association, Eli Lilly and Company, Pfizer, Biogen, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute on Aging, National Institute of Mental Health, National Center for Research Resources
Awards: Mapping Abnormal Neurodevelopment in Schizophrenia, Functional-anatomic exploration of cognitive control, Morphometry Biomedical Informatics Research Network, G5047191974, Alzheimer's Disease Research Center, Healthy Aging and Senile Dementia, Alzheimer's Disease Research Center, Alzheimer's Disease Neuroimaging Initiative (ADNI4), Antecedent Biomarkers for Alzheimer Disease: The Adult Children Study
OpenAlex ID: W7204491010