MICCAI 2022 Daily – Wednesday

32 DAILY MICCAI Wednesday The first platform to support these new Federated Learning APIs is NVIDIA FLARE, the federated learning platform developed by NVIDIA. We welcome the integration of other federated learning toolkits to the MONAI Federated Learning APIs to help build a common foundation for collaborative learning in medical imaging. MONAI Federated Learning Docs NVIDIA FLARE + MONAI Example Auto3D Segmentation Auto3D is a low-code framework that allows data scientists and researchers of any skill level to train models that can quickly segment regions of interest in data from 3D imaging modalities like CT and MRI. Developers can start with as little as 1-5 lines of code, resulting in a highly accurate segmentation model. By focusing on accuracy and including state-of-the-art models like Swin UNETR, DiNTS, and SegResNet, data scientists and researchers can utilize the latest and greatest algorithms to help maximize their productivity. Auto3D Tutorial Auto3D Segmentation Training and Inference workflow Developing for the Medical AI Project Lifecycle