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Article Synopsis
  • Medical volume data is increasing significantly, leading to challenges in organizing, storing, and processing large datasets.
  • The proposed solution is an end-to-end architecture for data compression using deep learning, featuring modules for downsampling, implicit neural representation, and super-resolution.
  • Experimental results show impressive compression rates of up to 97.5% while preserving high reconstruction quality, making it efficient for managing large medical data on GPUs.
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