Publications related to Statistical classification

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

OXFORD UNIV PRESS INC2021

Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health

David Atienza Alonso, Lara Orlandic, Adriana Arza Valdes

A person’s passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous mental health monitoring. In this work, we develop a custom experiment and Machi ...

2021

Fast screening of covariates in population models empowered by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...

2021

Development and evaluation of galaxy shape measurement algorithms for radio interferometric data

Vasileios Angelopoulos

The purpose of this Thesis is to develop, test, and characterize different models attempting to tackle the problem of measurement of galaxy shapes applied in interferometric observations. Shape measurement is a tool for estimating the underlying shear due ...

EPFL2021

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Marina Zapater Sancho, Tomas Teijeiro Campo, Valentin Alexandre Guy Gabeff, Leila Cammoun

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neur ...

2021

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Ian Smith, Sai Ganesh Sarvotham Pai, Masoud Sanayei

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and sys ...

2020

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Gökhan Yildirim

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural im ...

2020

Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera

Alexis Berne, Christophe Praz, Mathieu Oscar Schaer

A new method to automatically discriminate between hydrometeors and blowing snow particles on MultiAngle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detecte ...

COPERNICUS GESELLSCHAFT MBH2020

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Dionisije Sopic

In this thesis, I focus on monitoring of patients suffering from cardiovascular and neurological diseases through the use of wearable devices. The main diseases considered in this thesis are atrial fibrillation (AF), myocardial infarction (MI), and epileps ...

EPFL2020

Statistical classification

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Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health

Fast screening of covariates in population models empowered by machine learning

Development and evaluation of galaxy shape measurement algorithms for radio interferometric data

Multiple testing with test statistics following heavy-tailed distributions

Interpreting deep learning models for epileptic seizure detection on EEG signals

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health

Development and evaluation of galaxy shape measurement algorithms for radio interferometric data

Fast screening of covariates in population models empowered by machine learning

Multiple testing with test statistics following heavy-tailed distributions

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Interpreting deep learning models for epileptic seizure detection on EEG signals

Identification of blowing snow particles in images from a Multi-Angle Snowflake Camera

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction