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Medical Open Network for AI (MONAI), AI Toolkit for Healthcare Imaging
The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.
Privacy Preserving AI – Andrew Trask, OpenMined
Learn the basics of secure and private AI techniques, including federated learning and secure multi-party computation. In this talk, Andrew Trask of OpenMined highlights the importance of privacy preserving machine learning, and how to use privacy-focused tools like PySyft.
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From our ranking selected Rss
- Posted by Rami Al-Rfou, Research Scientist, Google Research It has […]
- Posted by Xianzhi Du, Software Engineer and Jaeyoun Kim, Technical […]
- Posted by Sara Beery, Student Researcher, and Jonathan Huang, Research […]
- Germany is starting its presidency of the EU Council today under the motto “Together for Europe’s recovery” With a prominent […] The post DIGITAL SME welcomes “Digital Sovereignty as a Central Theme” for Germany’s EU […]
- High dimensional black-box optimization has broad applications but remains a challenging problem to solve. Given a set of samples $\{\vx_i, […]
- Quantifying causal contribution via structure preserving interventions. (arXiv:2007.00714v1 [cs.AI])We introduce 'Causal Information Contribution (CIC)' and 'Causal Variance Contribution (CVC)' to quantify the influence of each variable in a […]
- The existence of adversarial examples capable of fooling trained neural network classifiers calls for a much better understanding of possible […]
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