How to evaluate deep learning for cancer diagnostics - factors and recommendations

Biochim Biophys Acta Rev Cancer. 2021 Apr;1875(2):188515. doi: 10.1016/j.bbcan.2021.188515. Epub 2021 Jan 26.

Abstract

The large volume of data used in cancer diagnosis presents a unique opportunity for deep learning algorithms, which improve in predictive performance with increasing data. When applying deep learning to cancer diagnosis, the goal is often to learn how to classify an input sample (such as images or biomarkers) into predefined categories (such as benign or cancerous). In this article, we examine examples of how deep learning algorithms have been implemented to make predictions related to cancer diagnosis using clinical, radiological, and pathological image data. We present a systematic approach for evaluating the development and application of clinical deep learning algorithms. Based on these examples and the current state of deep learning in medicine, we discuss the future possibilities in this space and outline a roadmap for implementations of deep learning in cancer diagnosis.

Keywords: Artificial Intelligence; Cancer diagnostics; Deep learning; Machine learning.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Algorithms
  • Big Data
  • Computational Biology / methods*
  • Deep Learning
  • Early Detection of Cancer
  • Humans
  • Machine Learning
  • Neoplasms / diagnosis*
  • Neoplasms / pathology