Using ProtoPNet for Interpretable Alzheimer’s Disease Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early detection of Alzheimerâs disease (AD) is significant for identifying of better treatment plans for the patients as the AD is not curable. On the other hand, lack of interpretability for the high performing prediction models might prevent incorporation of such models in clinical usage for AD detection. Accordingly, it is important to develop highly interpretable models which can create trust towards the prediction models by showing the factors that contribute to the modelsâ decisions. In this paper, we use ProtoPNet architecture in combination with popular pretrained deep learning models to add interpretability to the AD classifications over MRI scans from ADNI and OASIS datasets. We find that the ProtoPNet model with DenseNet121 architecture can reach 90 percent accuracy while providing explanatory illustrations of the modelâs reasonings for the generated predictions. We also note that, in most cases, the performances of the ProtoPNet models are slightly inferior to their black-box counterparts, however, their ability to provide reasoning and transparency in the prediction generation process can contribute to higher adoption of the prediction models in clinical practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it