Parkinson's Disease Mid-Brain Assessment using MR T2 Images
Bibliographic record
Abstract
The reduction of dopamine generating neurons in the brain regions known as substantia nigra (SN) is the reason for Parkinson's Disease (PD). To detect such symptom, for each subject, our algorithm only needs to analyze 3 slices around the center of a MRI DICOM volume, i.e., mid-brain area. In each slice, a window covering the SN becomes the region of interest (ROI) for further analysis. The ROIs are pre-processed by denoising and removing intensity non-uniformity. Local Binary Pattern (LBP) and Histogram Oriented Gradient (HOG) are used for feature extraction. Random Forest (RF) and Support Vector Machine (SVM) are used as classifiers with Principle Component Analysis (PCA) as feature reduction method. For evaluation, we use MRI T2 scans from the Parkinson's Progression Markers Initiative (PPMI) data set. We conducted experiments to illustrate the different classification capabilities of LBP, HOG and the fusion of these features for PD prognosis. Analysis shows that the SVM classifier with fusion feature descriptors has the most accurate classification outcome for PD assessment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".