Federated Learning-assisted Self-supervised CNN for Monkeypox Diagnosis
Bibliographic record
Abstract
Monkeypox (Mpox) is a contagious viral illness that affects both humans and animals, and its early diagnosis is critical for the effective management and prevention of this disease. This work proposes a federated learning-assisted self-supervised convolutional neural network (CNN) for Mpox identification from skin lesion images. The demand for domain experts for ground truth annotations is reduced with the help of self-supervised learning, as it can process unlabeled data. The self-supervision is driven by a framework called simple contrastive learning for representations (SimCLR), which extracts discriminative patterns of Mpox from the skin lesion images. Federated learning (FL), on the other hand, enables a privacy-preserved collaborative training approach to build the Mpox classifier on vast diverse datasets from several healthcare institutions, remotely. Thorough experimental study on a publicly available benchmark dataset, the proposed approach achieves competitive performances.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.002 | 0.008 |
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; both teacher heads agree on what is shown here.
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".