Generative Multiple-Instance Learning Models For Quantitative\n Electromyography
Bibliographic record
Abstract
We present a comprehensive study of the use of generative modeling approaches\nfor Multiple-Instance Learning (MIL) problems. In MIL a learner receives\ntraining instances grouped together into bags with labels for the bags only\n(which might not be correct for the comprised instances). Our work was\nmotivated by the task of facilitating the diagnosis of neuromuscular disorders\nusing sets of motor unit potential trains (MUPTs) detected within a muscle\nwhich can be cast as a MIL problem. Our approach leads to a state-of-the-art\nsolution to the problem of muscle classification. By introducing and analyzing\ngenerative models for MIL in a general framework and examining a variety of\nmodel structures and components, our work also serves as a methodological guide\nto modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets\nand on the MUSK1 dataset, one of the most widely used benchmarks for MIL.\n
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".