Can Anatomical Information Guide the Performance of Convolutional Neural Networks for Classifying Neurodegenerative Diseases Using Brain MRI?
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have been gaining outstanding success in classifying neurodegenerative diseases using brain magnetic resonance imaging (MRI) images. Given the highly informative content of brain MRI and the black-box nature of CNNs, it can be challenging to disentangle the decision-making process and identify cerebral regions that are relevant for the classification. In this study, we aimed to investigate the behavior of a 3D CNN for the classification of multiple system atrophy (MSA), a rare atypical parkinsonian syndrome, by exploiting a priori anatomical information related to this neurodegenerative disorder. We considered three regions of interest (ROIs) affected by pathological changes as input to a 3D CNN in combination with the T1-weighted MRI images. By comparing different CNN implementations and introducing a specific module accounting for the anatomical information, we achieved high accuracy (78–89%) in discriminating MSA patients from healthy controls. Furthermore, performances varied depending on the ROI used, with lower sensitivity for MSA patients when considering bigger regions. Instead, modifying the CNN with the anatomical gate module, an anatomically oriented attention mechanism, improved the classification using smaller regions. These findings represent an encouraging starting point to exploit fully disease-specific a priori knowledge and enhance classification performance while supporting better interpretability and advancing deep learning-based aid-to-diagnosis tools in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".