ecancermedicalscience

Review

Machine learning in oncology: a review

30 Jun 2020
Cecilia Nardini

Machine learning is a set of techniques that promise to greatly enhance our data-processing capability. In the field of oncology, ML presents itself with a wealth of possible applications to the research and the clinical context, such as automated diagnosis and precise treatment modulation. In this paper, we will review the principal applications of ML techniques in oncology and explore in detail how they work. This will allow us to discuss the issues and challenges that ML faces in this field, and ultimately gain a greater understanding of ML techniques and how they can improve oncological research and practice.

Article metrics: 133 views
133

Related Articles

Nigel P Murray, Sócrates Aedo, Cynthia Fuentealba, Eduardo Reyes, Aníbal Salazar, Eghon Guzman, Shenda Orrego
Luca Nicosia, Federica Ferrari, Anna Carla Bozzini, Antuono Latronico, Chiara Trentin, Lorenza Meneghetti, Filippo Pesapane, Maria Pizzamiglio, Nicola Balesetreri, Enrico Cassano
Sushmita Gordhandas, Ryan M Kahn, Charlotte Gamble, Nizam Talukdar, Brandon Maddy, Becky Baltich Nelson, Gulce Askin, Paul J Christos, Kevin Holcomb, Thomas A Caputo, Eloise Chapman-Davis, Melissa K Frey
Richard Ofori-Asenso, Oyepeju Ogundipe, Akosua Adom Agyeman, Ken Lee Chin, Mohsen Mazidi, Zanfina Ademi, Marie Louise De Bruin, Danny Liew