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Found 5 papers, 3 papers with code
Inclusive, Differentially Private Federated Learning for Clinical Data
Federated Learning (FL) offers a promising approach for training clinical AI models without centralizing sensitive patient data.
nnInteractive: Redefining 3D Promptable Segmentation
Crucially, it is the first method integrated into widely used image viewers (e. g., Napari, MITK), ensuring broad accessibility for real-world clinical and research applications.
Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
