Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning. Although foundation models have revolutionized language and vision tas... read more
Acute malnutrition remains a critical public health challenge across East Africa, contributing substantially to under-five morbidity and mortality. Early identification of at-risk children using predictive models could enhance timely intervention. Th... read more
We benchmarked histopathology foundation encoders paired with attention-based multiple instance learning (MIL) against convolutional neural networks (CNNs) to assess their robustness for endometrial cancer molecular classification (MMR-deficient, p53... read more
Flooding is one of the most disastrous natural hazards around the globe, causing enormous ecological and socio-economic losses; therefore, reliable assessment tools are required for informed risk management. This research proposes a hybrid flood susc... read more
PURPOSE: Ultrasound imaging has been routinely used for needle guidance due to its real-time capability and cost-efficiency. However, conventional ultrasound with fixed-angle transmission often suffers from reduced visualization of oblique needles wh... read more
Conventional end-to-end deep neural networks often degrade under domain shifts and require costly retraining when deployed in unpredictable, noisy environments. Inspired by biological brains, we propose a modular framework where each module is a recu... read more
Airport construction under non-stop operations presents unique safety challenges due to complex multi-factor interactions that traditional qualitative methods cannot adequately address. To address this, a study was conducted on 412 construction event... read more
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