49 citations · 123 across the 9 of their papers we have counts for
7 papers · 1 filter
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach +1
Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…
Learning-Based Quality Control for Cardiac MR Images
Giacomo Tarroni, Ozan Oktay, Wenjia Bai +9
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned…
Employing Weak Annotations for Medical Image Analysis Problems
Martin Rajchl, Lisa M. Koch, Christian Ledig +4
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation eff…
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
Martin Rajchl, Matthew C. H. Lee, Ozan Oktay +8
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the w…
Learning under Distributed Weak Supervision
Martin Rajchl, Matthew C. H. Lee, Franklin Schrans +7
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater,…