Case Study - Commercial Viability for AI-based Medical Segmentation

Learn How Deci Reduced the Latency of a SAUNet Model by 2X while Preserving Accuracy

The rise of deep neural network modeling applied to medical imaging has managed to automate parts of these processes with almost human-like accuracy. Unfortunately, their intense computing demands make them expensive for use in commercial environments.

In this case study, you will learn about:

  • The formidable challenge of MRI cardiac segmentation
  • Automation of medical imaging processes through deep neural network modeling
  • The intense computing demands and costs of these AI techniques
  • How to optimize runtime with AutoNAC technology, making the solution commercially viable

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