Search Results for author:
Found 5 papers, 1 papers with code
Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study
As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant.
Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study
In particular, the power spectral density (PSD) of low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha} and \b{eta}) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD).
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI
Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images.
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI
Nevertheless, they rely on large amounts of annotated data which is not common in the medical field.
Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data
Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa).
