Pathology Image Labeling Comes to MONAI

Pathology in MONAI Overview Video

Viewer Integration

MONAI Label now integrates with three pathology viewers, including QuPath, Digital Slide Archive or DSA, and CVAT. You can choose your favorite viewer and quickly start labeling pathology images today.

Pathology Viewer Integration for QuPath, DSA, and CVAT

Sample Applications

MONAI Label also provides new Pathology based sample applications that can be used as an easy starting point or as the basis for your custom annotation application.

Performance

One of the most significant pain points in the Pathology workflow is the performance of loading images and performing inference. MONAI Label focuses on performance throughout the Pathology Workflow, improving time to inference on patches and whole slide images and offering the ability to use RAPIDS cuCIM to speed up the loading of images.

Inference Performance

For performance on Patches and Whole Slide Images, both NuClick and DeepEdit perform significantly faster than typical Machine Learning Nuclei Detection, with DeepEdit performing up to six times faster than the standard NucleiDetection algorithm, as shown below. We measured performance with varying amounts of nuclei in the image, and the results show up to a 6x speedup in inference. This performance increase can mean the difference between 3 minutes for GPU-based inference or 20 minutes for CPU-based inference.

# of Nuclei Inference Performance

RAPIDS cuCIM

A significant portion of a typical deep learning pipeline is spent on I/O. For Pathology, this is the case since images are typically extremely large, and the process of loading and encoding these images can become a significant bottleneck. That’s why MONAI has integrated RAPIDS cuCIM as one of its optional image loaders.

SVS File Loading Performance for OpenSlide vs. cuCIM

Pathology Adoption of Deep Learning

MONAI Label is creating a starting point for Pathologists and Data scientists to work together and utilize the benefits of Deep Learning. By enabling a workflow that integrates directly into a Pathologist viewer and allowing for continuous learning, MONAI aims to accelerate the adoption of Deep Learning in Pathology.

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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