Learn How Deci Reduced the Latency of an AI-based Medical Segmentation, 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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