Nov 9, 2022 · We propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case- ...
Sep 24, 2024 · OBJECTIVE: To design an interpretable machine learning model which accurately predicts EEG protopatterns while providing an explanation of its ...
Nov 9, 2022 · OBJECTIVE: To design an interpretable machine learning model which accurately predicts EEG protopatterns while providing an explanation of its ...
We developed an interpretable deep-learning system that accurately classifies six patterns of potentially harmful EEG activity.
Missing: Mapping | Show results with:Mapping
May 23, 2024 · The interpretable design facilitates effective human–AI collaboration; this system may improve diagnosis and patient care in clinical settings.
We developed an interpretable deep-learning system that accurately classifies six types of potentially harmful EEG activity (Seizure, LPD, GPD, LRDA, GRDA, ...
Improving Clinician Performance in Classifying EEG Patterns on the Ictal–Interictal Injury Continuum Using Interpretable Machine Learning.
Mapping the ictal-interictal-injury continuum using interpretable machine learning. AJ Barnett, Z Guo, J Jing, W Ge, C Rudin, MB Westover. arXiv preprint ...
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Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning · Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M ...
Improving Clinician Performance in Classifying EEG Patterns on the Ictal-Interictal Injury Continuum Using Interpretable Machine Learning. Barnett A, Guo Z ...