65 citations · 114 across the 11 of their papers we have counts for
23 papers
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
Francesco Galati, Daniele Falcetta, Rosa Cortese +4
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality…
Binary domain generalization for sparsifying binary neural networks
Riccardo Schiavone, Francesco Galati, Maria A. Zuluaga
Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices. Despite their su…
JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images
Natalia Valderrama, Ioannis Pitsiorlas, Luisa Vargas +2
We propose the first joint-task learning framework for brain and vessel segmentation (JoB-VS) from Time-of-Flight Magnetic Resonance images. Unlike state-of-the-art vessel segmenta…
Sparsifying Binary Networks
Riccardo Schiavone, Maria A. Zuluaga
Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing com…
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
Julien Audibert, Pietro Michiardi, Frédéric Guyard +2
Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attenti…