Case Study

Commercial Viability for AI-based Medical Segmentation

Resource Featured Image

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

Complete the form to get immediate access to the case study.

Access Case Study Now

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")