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A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences
As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including co...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192570/ https://www.ncbi.nlm.nih.gov/pubmed/34141144 http://dx.doi.org/10.1016/j.csbj.2021.05.038 |