Search Results for author: Maria A. Zuluaga

Found 18 papers, 7 papers with code

Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?

no code implementations4 Apr 2022 Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga

In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets.

Time Series Unsupervised Anomaly Detection

AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics

no code implementations7 Feb 2022 Piera Riccio, Kristin Bergaust, Boel Christensen-Scheel, Juan-Carlos De Martin, Maria A. Zuluaga, Stefano Nichele

While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices.

EEG Fairness

Optimization Strategies in Multi-Task Learning: Averaged or Independent Losses?

no code implementations21 Sep 2021 Lucas Pascal, Pietro Michiardi, Xavier Bost, Benoit Huet, Maria A. Zuluaga

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 functions.

Multi-Task Learning

One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring

no code implementations9 Apr 2021 Laura M. Ferrari, Guy Abi Hanna, Paolo Volpe, Esma Ismailova, François Bremond, Maria A. Zuluaga

A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature.

EEG Event Detection

Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease

no code implementations5 Apr 2021 Maria A. Zuluaga, Alex F. Mendelson, M. Jorge Cardoso, Andrew M. Taylor, Sébastien Ourselin

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.

USAD: UnSupervised Anomaly Detection on Multivariate Time Series

2 code implementations KDD 2020 Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga

Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.

Time Series Unsupervised Anomaly Detection

Maximum Roaming Multi-Task Learning

1 code implementation17 Jun 2020 Lucas Pascal, Pietro Michiardi, Xavier Bost, Benoit Huet, Maria A. Zuluaga

Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.

Inductive Bias Multi-Task Learning

Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI

no code implementations5 Apr 2020 Maria A. Zuluaga, M. Jorge Cardoso, Sébastien Ourselin

Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function.

Motion Segmentation Right Ventricle Segmentation

Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting

2 code implementations16 Mar 2020 Rosa Candela, Pietro Michiardi, Maurizio Filippone, Maria A. Zuluaga

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 tour packages.


Multi-objective Consensus Clustering Framework for Flight Search Recommendation

no code implementations20 Feb 2020 Sujoy Chatterjee, Nicolas Pasquier, Simon Nanty, Maria A. Zuluaga

To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required.

Clustering Ensemble

Grey matter sublayer thickness estimation in themouse cerebellum

1 code implementation8 Jan 2019 Da Ma, Manuel J. Cardoso, Maria A. Zuluaga, Marc Modat, Nick. Powell, Frances Wiseman, Victor Tybulewicz, Elizabeth Fisher, Mark. F. Lythgoe, Sebastien Ourselin

In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.

Elastic Registration of Geodesic Vascular Graphs

no code implementations14 Sep 2018 Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

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-sectional clinical inference.

Computer Vision Graph Matching

VTrails: Inferring Vessels with Geodesic Connectivity Trees

no code implementations8 Jun 2018 Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

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 features such as spatial location, direction and scale.

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

no code implementations11 Oct 2017 Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren

Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.

Interactive Segmentation Medical Image Segmentation +1

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

1 code implementation3 Jul 2017 Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren

We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.

Brain Tumor Segmentation Interactive Segmentation +2

What is the distribution of the number of unique original items in a bootstrap sample?

no code implementations18 Feb 2016 Alex F. Mendelson, Maria A. Zuluaga, Brian F. Hutton, Sébastien Ourselin

The purpose of this report is to present the distribution of the number of unique original items in a bootstrap sample clearly and concisely, with a view to enabling other machine learning researchers to understand and control this quantity in existing and future resampling techniques.

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