Accurate classification of multi-type brain tumours through in vivo proton magnetic resonance spectroscopy remains a significant challenge. Conventional machine learning classifiers consider all reliably observed metabolites as features and classify all brain tumours simultaneously, but their performance is limited for rare tumour types.
Binary adaptive metabolite selection (BAMS) can significantly improve the classification performance for rare tumour types.
Philosophy
BAMS generalises the problem by considering only one specific brain tumour type and selecting significant biomarkers in each layer iteratively and dynamically.
Acknowledgements
The author acknowledges funding support from NIHR GOSH BRC.