TY - GEN
T1 - RF-Isolation
T2 - 17th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2021
AU - Mensi, Antonella
AU - Schiavi, Simona
AU - Petracca, Maria
AU - Graziano, Nicole
AU - Daducci, Alessandro
AU - Inglese, Matilde
AU - Bicego, Manuele
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Magnetic Resonance Imaging (MRI) is one of the tools used to identify structural and functional changes caused by multiple sclerosis, and by processing MR images, connectivity networks can be obtained. The analysis of structural connectivity networks of multiple sclerosis patients usually employs network-derived metrics, which are computed independently for each subject. We propose a novel representation of connectivity networks that is extracted from a model trained on the whole multiple sclerosis population: RF-Isolation. RF-Isolation is a vector encoding the disconnection of each region of interest with respect to all other regions. This feature can be easily captured by isolation-based outlier detection methods. We therefore reformulate the task as an outlier detection problem and propose a novel approach, called MS-ProxIF, based on a variant of Isolation Forest, a Random Forest-based outlier detection system, from which the representation is extracted. We test the representation via a set of classification experiments, involving 79 subjects, 55 of which suffer from multiple sclerosis. In particular, we compare favourably to the most used network-derived metrics in multiple sclerosis.
AB - Magnetic Resonance Imaging (MRI) is one of the tools used to identify structural and functional changes caused by multiple sclerosis, and by processing MR images, connectivity networks can be obtained. The analysis of structural connectivity networks of multiple sclerosis patients usually employs network-derived metrics, which are computed independently for each subject. We propose a novel representation of connectivity networks that is extracted from a model trained on the whole multiple sclerosis population: RF-Isolation. RF-Isolation is a vector encoding the disconnection of each region of interest with respect to all other regions. This feature can be easily captured by isolation-based outlier detection methods. We therefore reformulate the task as an outlier detection problem and propose a novel approach, called MS-ProxIF, based on a variant of Isolation Forest, a Random Forest-based outlier detection system, from which the representation is extracted. We test the representation via a set of classification experiments, involving 79 subjects, 55 of which suffer from multiple sclerosis. In particular, we compare favourably to the most used network-derived metrics in multiple sclerosis.
KW - Microstructure informed tractography
KW - Multiple sclerosis
KW - Proximity isolation forest
KW - Structural connectivity network
UR - http://www.scopus.com/inward/record.url?scp=85144251274&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-20837-9_13
DO - 10.1007/978-3-031-20837-9_13
M3 - Conference contribution
AN - SCOPUS:85144251274
SN - 9783031208362
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 169
BT - Computational Intelligence Methods for Bioinformatics and Biostatistics - 17th International Meeting, CIBB 2021, Revised Selected Papers
A2 - Chicco, Davide
A2 - Facchiano, Angelo
A2 - Tavazzi, Erica
A2 - Longato, Enrico
A2 - Vettoretti, Martina
A2 - Bernasconi, Anna
A2 - Avesani, Simone
A2 - Cazzaniga, Paolo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 15 November 2021 through 17 November 2021
ER -