869 citations · 1.5k across the 23 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…
AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics
Piera Riccio, Kristin Bergaust, Boel Christensen-Scheel +3
While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices. In this work, we pres…
Class-wise Thresholding for Robust Out-of-Distribution Detection
Matteo Guarrera, Baihong Jin, Tung-Wei Lin +3
We consider the problem of detecting OoD(Out-of-Distribution) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of sev…
Improved optimization strategies for deep Multi-Task Networks
Lucas Pascal, Pietro Michiardi, Xavier Bost +2
In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective f…
One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring
Laura M. Ferrari, Guy Abi Hanna, Paolo Volpe +3
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for ele…
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
Francesco Galati, Maria A. Zuluaga
Deep learning methods have reached state-of-the-art performance in cardiac image segmentation. Currently, the main bottleneck towards their effective translation into clinics requi…
Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease
Maria A. Zuluaga, Alex F. Mendelson, M. Jorge Cardoso +2
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image. I…
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
Vien Ngoc Dang, Francesco Galati, Rosa Cortese +7
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors…
Maximum Roaming Multi-Task Learning
Lucas Pascal, Pietro Michiardi, Xavier Bost +2
Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance. Nonetheless, the joint optimization of parameters with r…
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Rosa Candela, Pietro Michiardi, Maurizio Filippone +1
Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive…
Multi-objective Consensus Clustering Framework for Flight Search Recommendation
Sujoy Chatterjee, Nicolas Pasquier, Simon Nanty +1
In the travel industry, online customers book their travel itinerary according to several features, like cost and duration of the travel or the quality of amenities. To provide per…
Towards Quantifying Neurovascular Resilience
Stefano Moriconi, Rafael Rehwald, Maria A. Zuluaga +4
Whilst grading neurovascular abnormalities is critical for prompt surgical repair, no statistical markers are currently available for predicting the risk of adverse events, such as…
Grey matter sublayer thickness estimation in themouse cerebellum
Da Ma, Manuel J. Cardoso, Maria A. Zuluaga +7
The cerebellar grey matter morphology is an important feature to study neurodegenerative diseases such as Alzheimer's disease or Down's syndrome. Its volume or thickness is commonl…
Elastic Registration of Geodesic Vascular Graphs
Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager +3
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sect…
VTrails: Inferring Vessels with Geodesic Connectivity Trees
Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger +3
The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative…
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang, Wenqi Li, Maria A. Zuluaga +8
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate…
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang, Maria A. Zuluaga, Wenqi Li +8
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art…
What is the distribution of the number of unique original items in a bootstrap sample?
Alex F. Mendelson, Maria A. Zuluaga, Brian F. Hutton +1
Sampling with replacement occurs in many settings in machine learning, notably in the bagging ensemble technique and the .632+ validation scheme. The number of unique original item…