Publications by authors named "A L Routier"
Objectives: This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological reading.
Materials And Methods: The training dataset included 4381 bpMRI cases (3800 positive and 581 negative) across three continents, with 80% annotated using PI-RADS and 20% with Gleason Scores. The testing set comprised 328 cases from the PROSTATEx dataset, including 34% positive (GGG ≥ 2) and 66% negative cases.
Article Synopsis
- The Brain Imaging Data Structure (BIDS) is a community-created standard for organizing neuroscience data and metadata, helping researchers manage various modalities efficiently.
- The paper discusses the evolution of BIDS, including the guiding principles, extension mechanisms, and challenges faced during its development.
- It also highlights key lessons learned from the BIDS project, aiming to inspire and inform researchers in other fields about effective data organization practices.
Article Synopsis
- The Brain Imaging Data Structure (BIDS) is a collaborative standard designed to organize various neuroscience data and metadata.
- The paper details the history, principles, and mechanisms behind the development and expansion of BIDS, alongside the challenges it faces as it evolves.
- It also shares lessons learned from the project to help researchers in other fields apply similar successful strategies.
Myotonic dystrophy type 1 is an autosomal dominant multisystemic disorder affecting muscular and extra muscular systems, including the central nervous system. Cerebral involvement in myotonic dystrophy type 1 is associated with subtle cognitive and behavioural disorders, of major impact on socio-professional adaptation. The social dysfunction and its potential relation to frontal lobe neuropsychology remain under-evaluated in this pathology.
View Article and Find Full Text PDFClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.
Comput Methods Programs Biomed
June 2022
Background And Objective: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to help them avoid common pitfalls that will bias and discredit their results. Several tools have been proposed to help deep learning users design their framework for neuroimaging data sets. Software overview: We present here ClinicaDL, one of these software tools.
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