A cross-population framework for EEG Parkinson's detection using exhaustive 75 directional evaluations and nested validation shows asymmetric transfer and accuracy up to 94.1% when training diversity increases, supported by mixture risk theory.
A survey on transfer learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Sequential deep learning model classifies infectious keratitis images at 80% accuracy, outperforming ophthalmologists on a 120-image test set.
citing papers explorer
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Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection
A cross-population framework for EEG Parkinson's detection using exhaustive 75 directional evaluations and nested validation shows asymmetric transfer and accuracy up to 94.1% when training diversity increases, supported by mixture risk theory.
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Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
Sequential deep learning model classifies infectious keratitis images at 80% accuracy, outperforming ophthalmologists on a 120-image test set.