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.

Related Articles

Mohammad Saad Salim Naviwala, Mirza Rameez Samar, Daania Shoaib, Fizza Akbar, Romana Idrees, Yasmin Abdul Rashid
Johannes Matthias Weimer, Eva Kuhn, Michael Ludwig, Goodluck Lincoln Malle, Godfrid Kapipi, Valentin Sebastian Schäfer, Adnan Sadiq, Oliver Henke
Isabel Saffie-Vega, Sergio Muñoz-Navarro, Macarena Manríquez-Mimica, Jorge Sapunar-Zenteno
Raúl Sandoval-Ato, Patricia Coral-Gonzales, Sebastian Coronel-Arias, Luisa Espinoza-Mantilla, Grace Terrones-Chaparro, Victor Serna-Alarcón
Nicolás Duque Clavijo, Juana Catalina Figueroa Aguirre, Claudia del Pilar Agudelo Lopez, Andrés Armando Borda, Beatriz Wills, Guillermo Enrique Quintero Vega