The Clara Train SDK, running on top of optimized Tensorflow, provides the ability to add AI-Assisted annotation to any medical imaging viewer (annotation tool) and provides pre-trained AI Models and development tools to accelerate creation of AI Algorithms for medical imaging workflows. The Clara Train SDK consists of:
- AI assisted annotation APIs. Advanced features for Auto annotation and interactive annotation.
- Annotation Server. Provides pretrained models to the client application for Transfer Learning
- Unified Python based APIs. Exposes techniques like Transfer learning for training models and provides the ability to train from scratch.
- Pre-trained Models. Based on AH-Net, DenseNet, ResNet, Dextr3D packaged as complete 2D/3D Model Applications for organ based segmentation, classification and annotation.
Clara Train Framework
- Federated learning is a collaborative learning technique that allows for distributed training with multiple clients. With Clara Train v2.0 we bring privacy-preserving Federated Learning that enables researcher to collaborate and build AI Models without sharing private data.
- Automatic Mixed Precision(AMP) allows researchers to train with half precision and maintain network accuracy. AMP can reduce memory usage and provide significant speed ups to training process.
- Deterministic training on GPUs is now available in the SDK and is crucial to guarantee reproducibility for iterative experimentation.
- The option to use Smart Cache in new task specific ImagePipelines allows for faster and more efficient training by saving intermediate results and skipping repeated operations.
- New loss functions and models have been added.
NVIDIA brings its leadership position in modern artificial intelligence and deep learning to help automate the processing and understanding of images generated by medical scanners. The NVIDIA AI-assisted Annotation enables deep learning based applications by providing developers with tools that make it possible to speed up the annotation process, helping radiologists save time, and increase productivity
AI requires massive amounts of data. This is particularly true for industries such as healthcare. In order to build robust AI algorithms, hospitals and medical institutions often need to collaboratively share and combine their local knowledge. However, this is challenging because patient data is private by nature. It is vital to train the algorithms without compromising privacy.
NVIDIA’s latest release of Clara Train SDK, which features Federated Learning (FL), makes this possible with NVIDIA EGX, the edge AI computing platform. The common collaborative learning paradigm enables different sites to securely collaborate, train, and contribute to a global model. Since only partial model weights are shared with the global model from each site, privacy can be preserved and the data is less exposed to model inversion
Medical Open Network for AI – Toolkit for Healthcare Imaging
- Develop a community of academic, industrial and clinical researchers collaborating and working on a common foundation of standardized tools.
- Create a state-of-the-art, end-to-end training toolkit for healthcare imaging.
- Provide academic and industrial researchers with the optimized and standardized way to create and evaluate models
