TY - GEN
T1 - This EEG Looks Like These EEGs
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
AU - Tang, Dennis
AU - Donnelly, Jon
AU - Barnett, Alina Jade
AU - Semenova, Lesia
AU - Jing, Jin
AU - Hadar, Peter
AU - Karakis, Ioannis
AU - Selioutski, Olga
AU - Zhao, Kehan
AU - Westover, M. Brandon
AU - Rudin, Cynthia
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn towards machine learning for help. Although deep learning algorithms have shown state-of-the-art accuracy on this task, most models are uninterpretable and cannot justify their conclusions. Absent the ability to understand model reasoning, doctors cannot leverage their expertise to identify incorrect model predictions and intervene accordingly. To improve human-model interaction, we introduce ProtoEEG-kNN, an inherently interpretable IED-detection model that follows a simple case-based reasoning process. Specifically, ProtoEEG-kNN compares input EEGs to samples from the training set that contain similar IED morphology (shape) and spatial distribution (location). We show that ProtoEEG-kNN can achieve state-of-the-art accuracy while providing visual explanations that experts prefer over existing approaches.
AB - The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn towards machine learning for help. Although deep learning algorithms have shown state-of-the-art accuracy on this task, most models are uninterpretable and cannot justify their conclusions. Absent the ability to understand model reasoning, doctors cannot leverage their expertise to identify incorrect model predictions and intervene accordingly. To improve human-model interaction, we introduce ProtoEEG-kNN, an inherently interpretable IED-detection model that follows a simple case-based reasoning process. Specifically, ProtoEEG-kNN compares input EEGs to samples from the training set that contain similar IED morphology (shape) and spatial distribution (location). We show that ProtoEEG-kNN can achieve state-of-the-art accuracy while providing visual explanations that experts prefer over existing approaches.
KW - Deep Learning
KW - Epilepsy Diagnosis
KW - Interpretability
UR - https://www.scopus.com/pages/publications/105018086147
U2 - 10.1007/978-3-032-05185-1_59
DO - 10.1007/978-3-032-05185-1_59
M3 - Conference contribution
AN - SCOPUS:105018086147
SN - 9783032051844
T3 - Lecture Notes in Computer Science
SP - 615
EP - 625
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Park, Jinah
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 September 2025 through 27 September 2025
ER -