The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variabilit...
Data heterogeneity critically limits distributed artificial intelligence (AI) in medical imaging. We propose HeteroSync Learning (HSL), a privacy-preserving framework that addresses heterogeneity through: (1) Shared Anchor Task (SAT) for cross-node r...
Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strateg...
Assessing the efficacy of radiotherapy in patients with high-grade gliomas (HGGs) is challenging due to the occurrence of pseudo-progression and radionecrosis. This study introduces a directed graph network leveraging MR image features at multiple ti...
Deep learning models are prone to failure when inferring upon out-of-distribution (OOD) data, i.e. data whose features fundamentally differ from those in the training set. Existing OOD measures often lack sensitivity to the subtle image variations en...
A brain tumor is one of the life-threatening neurological conditions affecting millions of people worldwide. Early diagnosis and classification of brain tumor types facilitate prompt treatment, thereby increasing the patient's chances of survival. Th...
Kidney stone disease is a common syndrome and a recurring one, where it bears a 50% chance of being manifested again within ten years and may lead to serious complications like ureteral obstruction and unbearable pain. If timely intervention is consi...
Mammography is a routine imaging technique used by radiologists to detect breast lesions, such as tumors and lumps. Precise lesion detection is critical for early treatment and diagnosis planning. Lesion detection and segmentation are still problemat...
Breast cancer diagnosis via histopathology image analysis is a complex and subjective process. While deep learning has emerged as a powerful tool for automation, achieving high accuracy across diverse cancer subtypes and magnification levels remains ...
Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, co...
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