A DEEP LEARNING FRAMEWORK TO CHARACTERIZE NOISY LABELS IN EPILEPTOGENIC ZONE LOCALIZATION USING FUNCTIONAL CONNECTIVITY.

Resting-sate fMRI (rs-fMRI) has emerged as a viable tool to localize the epileptogenic zone (EZ) in medication refractory focal epilepsy patients. However, due to clinical protocol, datasets with reliable labels for the EZ are scarce. Some studies have used the entire resection area from post-operative structural T1 scans to act as the ground truth EZ labels during training and testing. These labels are subject to noise, as usually the resection area will be larger than the actual EZ tissue. We develop a mathematical framework for characterizing noisy labels in EZ localization. We use a multi-task deep learning framework to identify both the probability of a noisy label as well as the localization prediction for each ROI. We train our framework on a simulated dataset derived from the Human Connectome Project and evaluate it on both the simulated and a clinical epilepsy dataset. We show superior localization performance in our method against published localization networks on both the real and simulated dataset.