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论文ICLR 2026 Poster2026 年trustworthy medical AI

超越分类准确率:Neural-MedBench 与深层推理基准的必要性

ICLR 2026 Poster accepted paper at ICLR 2026. Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG). Clinical workflows, however, remain constrained by labor-intensive manual review. At the same time, existing data-driven approaches are typically developed on single-center datasets that are inconsistent in format and metadata, lack standardized benchmarks, and rarely release pathological event annotations, creating barriers to reproducibility, cross-center validation, and clinical relevance. Code/project link: https://omni-ieeg.github.io/omni-ieeg/; https://github.com/Omni-iEEG/Omni-iEEG

论文默认配图 - 医学影像计算

论文详情

英文标题
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
作者
Chenda Duan, Yipeng Zhang, Sotaro Kanai, Yuanyi Ding, Atsuro Daida, Pengyue Yu, Tiancheng Zheng, Naoto Kuroda, Shaun A. Hussain, Eishi Asano, Hiroki Nariai, vwani Roychowdhury
期刊/会议
ICLR 2026 Poster
发表年份
2026 年
研究方向
trustworthy medical AI