From AutoML powered development to cloud-native deployment, MONAI marches forward with four new releases.

MONAI Core v0.8

The first new release is MONAI Core v0.8 which expands on the available learning methods, including Self-Supervised and Multi-Instance learning support. It also includes a new AutoML technique called Differentiable Network Topology Search, or DiNTS, and new visualization techniques for the various transforms already available in MONAI.

Self-Supervised Learning
Multi-Instance Learning (MIL) for Whole-Slide Images (WSI)
Differentiable Neural Network Topology Search (DiNTS)
Transformer Visualization using MONAI MatShow3D API

MONAI Label v0.3

The second new release is MONAI Label v0.3 which includes multi-label segmentation for existing applications, increased performance by supporting multi-GPU training, and better Active Learning User Experience.

MONAI Deploy

The third new release is MONAI Deploy App SDK v0.2, which includes two new base operators for DICOM interactions: one for DICOM Series Selection and another exporting DICOM Structured Reports SOP for classification results.

MONAI Inference Service allows for deployment on Kubernetes using Helm
  • Upload inputs via a REST API request and make them available to the MAP container.
  • Provision resources for the MAP container.
  • Provide outputs of the MAP container to the client who made the request.

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MONAI Medical Open Network for AI

MONAI Medical Open Network for AI

MONAI framework is a PyTorch-based open-source foundation for deep learning in healthcare imaging. It is domain-optimized, freely available and community backed