Public articles linked to the same research event.
medRxiv This work presents ISDS-MRI, a unified framework that uses graph neural networks and sequence-specific feature modeling to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI while accommodating incomplete sequence combinations; evaluated across multiple public MRI datasets and a newly curated BGD-MRIS dataset of 532 scans from three hospitals in Bangladesh, it achieves a Dice score of 0.725, an AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice, 2.6% in detection, and reducing volume relative error by 1.9%.
This work presents ISDS-MRI, a unified framework that uses graph neural networks and sequence-specific feature modeling to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI while accommodating incomplete sequence combinations; evaluated across multiple public MRI datasets and a newly curated BGD-MRIS dataset of 532 scans from three hospitals in Bangladesh, it achieves a Dice score of 0.725, an AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice, 2.6% in detection, and reducing volume relative error by 1.9%.
This work presents ISDS-MRI, a unified framework that uses graph neural networks and sequence-specific feature modeling to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI while accommodating incomplete sequence combinations; evaluated across multiple public MRI datasets and a newly curated BGD-MRIS dataset of 532 scans from three hospitals in Bangladesh, it achieves a Dice score of 0.725, an AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice, 2.6% in detection, and reducing volume relative error by 1.9%.
This work presents ISDS-MRI, a unified framework that uses graph neural networks and sequence-specific feature modeling to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI while accommodating incomplete sequence combinations; evaluated across multiple public MRI datasets and a newly curated BGD-MRIS dataset of 532 scans from three hospitals in Bangladesh, it achieves a Dice score of 0.725, an AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice, 2.6% in detection, and reducing volume relative error by 1.9%.