Controlled experiments attribute cross-subject EEG classification degradation to inter-subject variability in multi-class tasks and shortcut learning in single-class tasks.
Medformer: A multi-granularity patching transformer for medical time-series classification
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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PatchECG applies masked patch training and disordered attention to handle asynchronous and partially missing ECG signals from varied layouts, reaching average AUROC 0.835 on simulated conditions and 0.778 on real hospital images for atrial fibrillation.
citing papers explorer
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What Causes Performance Degradation in Cross-Subject EEG Classification?
Controlled experiments attribute cross-subject EEG classification degradation to inter-subject variability in multi-class tasks and shortcut learning in single-class tasks.
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Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images
PatchECG applies masked patch training and disordered attention to handle asynchronous and partially missing ECG signals from varied layouts, reaching average AUROC 0.835 on simulated conditions and 0.778 on real hospital images for atrial fibrillation.