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Abstract The segmentation of spectral domain optical coherence tomography (SD-OCT) images for diabetic macular edema (DME) using deep learning technology has key challenges such as data privacy, computational cost, and information uncertainty. To address these, we present an uncertainty-informed neural network within sequential federated learning (UINN-SFL) for segmentation of DME.
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