Machine learning in oncology: a review
Cecilia Nardini
Abstract
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.
Topics & Concepts
MedicineContext (archaeology)Field (mathematics)Set (abstract data type)Clinical OncologyMedical physicsPrecision oncologyPrincipal (computer security)Clinical PracticeRadiation oncologyData scienceArtificial intelligenceComputer scienceInternal medicineCancerRadiation therapyBiologyProgramming languagePure mathematicsOperating systemFamily medicinePaleontologyMathematicsAI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education