Machine learning (ML) and deep learning (DL) applications have gained popularity in the field of neuroimaging in recent years. Here, we present a comparison between a state-of-the-art gradient boosting technique, the extreme gradient boosting (XGBoost), and a recently developed DL method, TabPFN, to assess the prediction of cognitive deficit in a large pathological population through structural and functional MRI markers. Overall, our results showed that conventional ML might still be the preferable choice for noisy tabular datasets (like neuroimaging data), also for their better explainability.
XGBoost vs. TabPFN in Neuroimaging Machine Learning-based analysis / De Rosa, A. P.; D'Ambrosio, A.; Marzi, C.; Cirillo, M.; Bisecco, A.; Altieri, M.; Diciotti, S.; Rocca, M. A.; De Stefano, N.; Pantano, P.; Filippi, M.; Tedeschi, G.; Gallo, A.; Esposito, F.. - (2023). (Intervento presentato al convegno 8th National Congress of Bioengineering, GNB 2023 tenutosi a Padova nel 2st1 - 23rd June 2023).
XGBoost vs. TabPFN in Neuroimaging Machine Learning-based analysis
Rocca M. A.;Filippi M.;
2023-01-01
Abstract
Machine learning (ML) and deep learning (DL) applications have gained popularity in the field of neuroimaging in recent years. Here, we present a comparison between a state-of-the-art gradient boosting technique, the extreme gradient boosting (XGBoost), and a recently developed DL method, TabPFN, to assess the prediction of cognitive deficit in a large pathological population through structural and functional MRI markers. Overall, our results showed that conventional ML might still be the preferable choice for noisy tabular datasets (like neuroimaging data), also for their better explainability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
