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

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.

Automated summary

This article examines the application of deep learning algorithms in cancer diagnosis, emphasizing the improvement in predictive performance as data volume increases. Examples of deep learning algorithms in cancer diagnosis using clinical, radiological, and pathological image data are presented, along with a systematic approach for evaluating and implementing these algorithms. The future possibilities and roadmap for implementing deep learning in cancer diagnosis are also discussed based on current state of deep learning in medicine.