Search Results for author: Ninon Burgos

Found 15 papers, 6 papers with code

Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

no code implementations20 Nov 2023 Maëlys Solal, Ravi Hassanaly, Ninon Burgos

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data.

Unsupervised Anomaly Detection

Reproducibility in machine learning for medical imaging

no code implementations12 Sep 2022 Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos

Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge.

Interpretability of Machine Learning Methods Applied to Neuroimaging

no code implementations14 Apr 2022 Elina Thibeau-Sutre, Sasha Collin, Ninon Burgos, Olivier Colliot

Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context.

BIG-bench Machine Learning

Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

2 code implementations30 Apr 2021 Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos, Stéphanie Allassonnière

In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder.

Data Augmentation Specificity

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

1 code implementation16 Apr 2021 Simona Bottani, Ninon Burgos, Aurélien Maire, Adam Wild, Sebastian Ströer, Didier Dormont, Olivier Colliot

In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse.

Visualization approach to assess the robustness of neural networks for medical image classification

no code implementations19 Nov 2019 Elina Thibeau Sutre, Olivier Colliot, Didier Dormont, Ninon Burgos

We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer's disease and that the visualization approach extract coherent longitudinal patterns.

General Classification Image Classification +1

Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease

no code implementations21 Sep 2017 Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot

The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features.

Benchmarking Classification +1

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