4 research outputs found
Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease
Background: Although convolutional neural networks (CNN) achieve high
diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on
magnetic resonance imaging (MRI) scans, they are not yet applied in clinical
routine. One important reason for this is a lack of model comprehensibility.
Recently developed visualization methods for deriving CNN relevance maps may
help to fill this gap. We investigated whether models with higher accuracy also
rely more on discriminative brain regions predefined by prior knowledge.
Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI
scans of patients with dementia and amnestic mild cognitive impairment (MCI)
and verified the accuracy of the models via cross-validation and in three
independent samples including N=1655 cases. We evaluated the association of
relevance scores and hippocampus volume to validate the clinical utility of
this approach. To improve model comprehensibility, we implemented an
interactive visualization of 3D CNN relevance maps.
Results: Across three independent datasets, group separation showed high
accuracy for AD dementia vs. controls (AUC0.92) and moderate accuracy for
MCI vs. controls (AUC0.75). Relevance maps indicated that hippocampal
atrophy was considered as the most informative factor for AD detection, with
additional contributions from atrophy in other cortical and subcortical
regions. Relevance scores within the hippocampus were highly correlated with
hippocampal volumes (Pearson's r-0.86, p<0.001).
Conclusion: The relevance maps highlighted atrophy in regions that we had
hypothesized a priori. This strengthens the comprehensibility of the CNN
models, which were trained in a purely data-driven manner based on the scans
and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code
available on GitHu
Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges
International audiencePurpose of review. Machine learning (ML) is an artificial intelligence technique that allows computers to perform a task without being explicitly programmed. ML can be used to assist diagnosis and prognosis of brain disorders. While the earliest papers date from more than ten years ago, research increases at a very fast pace. Recent findings. Recent works using ML for diagnosis have moved from classification of a given disease versus controls to differential diagnosis. Intense research has been devoted to the prediction of the future patient state. While a lot of earlier works focused on neuroimaging as data source, the current trend is on the integration of multimodal. In terms of targeted diseases, dementia remains dominant, but approaches have been developed for a wide variety of neurological and psychiatric diseases. Summary. ML is extremely promising for assisting diagnosis and prognosis in brain disorders. Nevertheless, we argue that key challenges remain to be addressed by the community for bringing these tools in clinical routine: good practices regarding validation and reproducible research need to be more widely adopted; extensive generalization studies are required; interpretable models are needed to overcome the limitations of black-box approaches