With OpenMined, an AI model can be governed by multiple owners and trained securely on an unseen, distributed dataset.The mission of the OpenMined community is to create an accessible ecosystem of tools for private, secure, multi-owner governed AI
OpenMined: open source to make privacy-preserving of AI technologies Leer más »
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.
Medical Open Network for AI (MONAI), AI Toolkit for Healthcare Imaging Leer más »
Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data
Machine learning in medicine: a practical introduction Leer más »
In February 2020, the European Commission (EC) published a white paper entitled, On Artificial Intelligence – A European approach to excellence and trust. This paper outlines the EC’s policy options for the promotion and adoption of artificial intelligence (AI) in the European Union. We reviewed this paper and published a response addressing the EC’s plans to build an “ecosystem of excellence” and an “ecosystem of trust,” as well as the safety and liability implications of AI, the internet of things (IoT), and robotics.
Montreal AI Ethics Institute: Response to the European Commission’s white paper on AI Leer más »
A key challenge to making effective use of evolutionary algorithms (EAs) is to choose appropriate settings for their parameters. However, the appropriate parameter setting generally depends on the structure of the optimization problem, which is often unknown to the user. Non‐deterministic parameter control mechanisms adjust parameters using information obtained from the evolutionary process.
A Rigorous Analysis of Self‐Adaptation in Discrete Evolutionary Algorithms Leer más »
To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation Leer más »
In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in a standard benchmark using the CARLA driving simulator.
Explaining Autonomous Driving by Learning End-to-End Visual Attention Leer más »
Show how to onboard AI tools as re-usable building blocks that then can be used to easily compose AI pipelines in the AI4EU Experiments visual editor
Multi-presenter format with exciting Speakers from the current European ICT research projects AI4EU (www.ai4eu.eu) and Helios (helios-social.com/) as well as Guest Speakers.
Yooneeque has made digitalisation its motto. An artificial intelligence called YOONA is the fashion designer here. This time again during the Berlin Fashion Week the latest outputs of the software were presented