Weakly Supervised Multiple-Instance Learning for Seizure Onset Zone Identification

Accurate localization of the seizure onset zone (SOZ) is essential for planning resection or ablation in patients with focal drug-resistant epilepsy (DRE). However, the true SOZ cannot be directly observed during clinical evaluation, so precise electrode-level annotations are rarely available. To address this limitation, we developed a weakly supervised multiple-instance learning (MIL) framework in which labels were constructed from electrode resection status and postsurgical outcome. A shared gated recurrent unit (GRU) with temporal attention encoded each electrode's peri-onset neural fragili

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