A Comprehensive Low-Code Solution for Large-Scale 3D Medical Image Segmentation with MONAI Core and Auto3DSeg

, Senior Research Scientist, NVIDIA
, Senior Applied Research Scientist, NVIDIA
, Technical Product Manager, NVIDIA
, Applied Research Scientist, NVIDIA

Learn about Auto3DSeg, a low-code framework for building 3D medical image segmentation models using MONAI. Researchers and data scientists need a common foundational framework to perform training experiments and compare against the state of the art. MONAI provides domain-specific implementations to help kick-start development and research and integrate them into a framework called Auto3DSeg. Auto3DSeg makes this process easy by creating a framework that automates the data analysis, training pipeline, hyperparameter tuning, and ensembling of multiple models together with only a few lines of code - allowing data scientists and researchers of any skill level to train state-of-the-art models that can quickly segment regions of interest in data from 3D imaging modalities like CT and MRI. You'll get a short introduction to MONAI Core and then take a deep dive into the more technical features of Auto3DSeg by training your own segmentation model. 

 

Prerequisite(s): 

 

  • Basics in Deep Learning and PyTorch recommended 

 

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活动: GTC Digital Spring
日期: March 2023
话题: Deep Learning - Training
行业: 医疗健康与生命科学
级别: 中级技术
语言: 英语
话题: Deep Learning
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