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Among these approaches, binning methods emerge with the most favorable calibration results, with histogram binning yielding the lowest ECE and MCE metrics, ranging between 0.27-0.32% and 0.19-0.28%, respectively, while also improving accuracy. Isotonic regression closely follows, albeit with lower accuracy values.
This study investigates post-processing calibration methods for the XCM model across different sleep stages.
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Oct 16, 2024 · We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data.
This research explores the potential of leveraging sleep-related electroencephalography signals, obtained via polysomnography (PSG), for the early detection ...
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We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (N=10,875): (1) common test, (2) ...
Nov 7, 2024 · Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks. Azadeh Sadat Mozafari; Hugo Siqueira Gomes ; Prevalence ...
We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data.
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Background: Machine learning and data mining techniques have been successfully applied on MRI images for detecting Alzheimer's disease (AD).
Feb 1, 2024 · We develop a self-calibrating surface-enhanced Raman scattering (SERS)-lateral flow immunoassay (LFIA) biosensor for quantitative analysis of amyloid-β1-42 (Aβ ...
Nov 26, 2024 · Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic ...