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Found 5 papers, 2 papers with code
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data.
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions.
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification
When further applied to the task of predicting which patients with mild cognitive impairment will be diagnosed with Alzheimer's disease within two years, the model achieves state-of-the-art accuracy with no additional training.
Machine learning and glioma imaging biomarkers
Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).
An Update on Machine Learning in Neuro-oncology Diagnostics
Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring.
