Editorial: The use of deep learning in mapping and diagnosis of cancers

Abstract

Editorial on the Research TopicThe use of deep learning in mapping and diagnosis of cancers Deep Learning (DL) is a subset and an augmented version of Machine Learning (ML), which in turn is a subgroup of Artificial Intelligence (AI), that uses layers of neural networks, similar to human brain, for performing complex tasks quickly and accurately. AI can recognize patterns in a large volume of data and extract characteristics imperceptible to the human eye (1). Convolutional Neural Network (CNN) is the most commonly used network of DL, which contains multiple layers, with weighted connections between neurons that are trained iteratively to improve performance. DL can be supervised or unsupervised, but most of the practical uses of DL in cancer has been with supervised learning where labelled images are used for data training (2). Despite the growing number of uses of DL in cancer mapping and diagnosis, there are uncharted territories in DL which remain to be explored to utilize it to its full capacity. Also, in spite of the revolution in cancer research that DL has ushered in, there are a lot of challenges to overcome, before DL can be widely used and accepted in every corner of the world. Role of DL in oncologyThere has been an unprecedented surge in DL based research in oncology due to the availability of big data, powerful hardware and robust algorithms. Screening and diagnosis of cancer, prediction of treatment response, and survival outcome and recurrence prediction, are the various roles of ML and DL in cancer management. AI algorithms integrated with clinical decision support (CDS) tools can automatically mine electronic health record (EHR) and identify cohort that would benefit maximum from Frontiers in Oncology frontiersin.org 01