"Accelerating Deep Learning for Medical Image Analysis"
Version 1"The processing of large-scale, high-resolution medical images often leads to substantial computational requirements, increased GPU memory consumption, and longer training durations. To overcome these challenges, this work proposes an optimized deep learning framework for diabetic retinopathy prediction, integrating three key optimization strategies within a unified pipeline: mixed-precision training to reduce memory usage, Accelerated Linear Algebra (XLA) compilation to improve execution effici
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"The processing of large-scale, high-resolution medical images often leads to substantial computational requirements, increased GPU memory consumption, and longer training durations. To overcome these challenges, this work proposes an optimized deep learning framework for diabetic retinopathy prediction, integrating three key optimization strategies within a unified pipeline: mixed-precision training to reduce memory usage, Accelerated Linear Algebra (XLA) compilation to improve execution effici