Machine learning approaches achieve equivalent AUROC of 1.0 and near-perfect accuracy to deep learning for OOD detection on over 60,000 medical images but with substantially lower end-to-end latency.
Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions
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A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection
Machine learning approaches achieve equivalent AUROC of 1.0 and near-perfect accuracy to deep learning for OOD detection on over 60,000 medical images but with substantially lower end-to-end latency.