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

Prateek Das, Sujeet Kumar, Raghwesh Ranjan, Pradeep Arumugam, Nilesh Dhole, Rohit Kumar Kori, Anil Yadav, Anil Singh, Vikramjit Kanwar, Neha Singh
Anelisa K Coutinho, Yazmin Carolina Blanco Vazquez, Markus Andret Cavalcante Gifoni, Angela Marie Jansen, Juan Manuel O’Connor, Juan Carlos Samamé Pérez-Vargas, Mariana Rico-Restrepo, Gayatri Sanku, Guillermo Mendez
Pengkhun Nov, Yangfeng Zhang, Duanyu Wang, Syphanna Sou, Socheat Touch, Samnang Kouy, Virak Vicheth, Lilin Li, Xiang Liu, Changqian Wang, Peizan Ni, Qianzi Kou, Ying Li, Chongyang Zheng, Arzoo Prasai, Wen Fu, Wandan Li, Kunpeng Du, Jiqiang Li
Etienne Okobalemba Atenguena, Joseph Francis Nwatsock, Berthe Sabine Esson Mapoko, Lionel Fossa Tabola, Kenn Chi Ndi, Jérôme Boombhi, Paul Ndom