Search Results for author: Maria A. Zuluaga

Found 23 papers, 11 papers with code

Binary domain generalization for sparsifying binary neural networks

1 code implementation23 Jun 2023 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.

Binarization Domain Generalization

JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images

1 code implementation16 Apr 2023 Natalia Valderrama, Ioannis Pitsiorlas, Luisa Vargas, Pablo Arbeláez, Maria A. Zuluaga

These results show the adequacy of JoB-VS for the challenging task of vessel segmentation in complete TOF-MRA images.

Data Augmentation Segmentation

Fairness and bias correction in machine learning for depression prediction: results from four study populations

1 code implementation10 Nov 2022 Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir

Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations.

Fairness Model Selection

Sparsifying Binary Networks

no code implementations11 Jul 2022 Riccardo Schiavone, Maria A. Zuluaga

Our experiments confirm that SBNNs can achieve high compression rates, without compromising generalization, while further reducing the operations of BNNs, making SBNNs a viable option for deploying DNNs in cheap, low-cost, limited-resources IoT devices and sensors.

Binarization Quantization +2

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.

BIG-bench Machine Learning Time Series +2

Improved optimization strategies for deep Multi-Task Networks

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

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 Time Series Analysis +1

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.

Anatomy Motion Segmentation +2

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 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.

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.

Image Segmentation Interactive Segmentation +4

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 Image Segmentation +4

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.

BIG-bench Machine Learning

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