Generative Multiple-Instance Learning Models For Quantitative Electromyography

Tameem Adel, Ruth Urner, Benn Smith, Daniel Stashuk, Dan Lizotte
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:5-14, 2013.

Abstract

We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task of facilitating the di- agnosis of neuromuscular disorders using sets of motor unit potential trains (MUPTs) de- tected within a muscle which can be cast as a MIL problem. Our approach leads to a state- of-the-art solution to the problem of muscle classification. By introducing and analyzing generative models for MIL in a general frame- work and examining a variety of model struc- tures and components, our work also serves as a methodological guide to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets and on the MUSK1 dataset, one of the most widely used bench- marks for MIL.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-adel13a, title = {Generative Multiple-Instance Learning Models For Quantitative Electromyography}, author = {Adel, Tameem and Urner, Ruth and Smith, Benn and Stashuk, Daniel and Lizotte, Dan}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {5--14}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/adel13a/adel13a.pdf}, url = {https://proceedings.mlr.press/r11/adel13a.html}, abstract = {We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task of facilitating the di- agnosis of neuromuscular disorders using sets of motor unit potential trains (MUPTs) de- tected within a muscle which can be cast as a MIL problem. Our approach leads to a state- of-the-art solution to the problem of muscle classification. By introducing and analyzing generative models for MIL in a general frame- work and examining a variety of model struc- tures and components, our work also serves as a methodological guide to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets and on the MUSK1 dataset, one of the most widely used bench- marks for MIL.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Generative Multiple-Instance Learning Models For Quantitative Electromyography %A Tameem Adel %A Ruth Urner %A Benn Smith %A Daniel Stashuk %A Dan Lizotte %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-adel13a %I PMLR %P 5--14 %U https://proceedings.mlr.press/r11/adel13a.html %V R11 %X We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task of facilitating the di- agnosis of neuromuscular disorders using sets of motor unit potential trains (MUPTs) de- tected within a muscle which can be cast as a MIL problem. Our approach leads to a state- of-the-art solution to the problem of muscle classification. By introducing and analyzing generative models for MIL in a general frame- work and examining a variety of model struc- tures and components, our work also serves as a methodological guide to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets and on the MUSK1 dataset, one of the most widely used bench- marks for MIL. %Z Reissued by PMLR on 04 October 2026.
APA
Adel, T., Urner, R., Smith, B., Stashuk, D. & Lizotte, D.. (2013). Generative Multiple-Instance Learning Models For Quantitative Electromyography. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:5-14 Available from https://proceedings.mlr.press/r11/adel13a.html. Reissued by PMLR on 04 October 2026.

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