High-performance prediction models for prostate cancer radiomics

High-performance prediction models for prostate cancer radiomics

When researchers are faced with building machine learning (ML) radiomic models, the first choice they have to make is what model to use. Naturally, the goal is to use the model with the best performance. But what is the best model? It is well known in ML that modern techniques such as gradient boost...

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Bibliographic Details
Main Authors: Lars Johannes Isaksson, Marco Repetto, Paul Eugene Summers, Matteo Pepa, Mattia Zaffaroni, Maria Giulia Vincini, Giulia Corrao, Giovanni Carlo Mazzola, Marco Rotondi, Federica Bellerba, Sara Raimondi, Zaharudin Haron, Sarah Alessi, Paula Pricolo, Francesco Alessandro Mistretta, Stefano Luzzago, Federica Cattani, Gennaro Musi, Ottavio De Cobelli, Marta Cremonesi, Roberto Orecchia, Davide La Torre, Giulia Marvaso, Giuseppe Petralia, Barbara Alicja Jereczek-Fossa
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:Informatics in Medicine Unlocked
Subjects:
Radiomics
Prostate cancer
Deep learning
Gradient boost
Online Access:http://www.sciencedirect.com/science/article/pii/S2352914823000035

Internet

http://www.sciencedirect.com/science/article/pii/S2352914823000035

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