A primer for understanding radiology articles about machine learning and deep learning.

By T. Nakaura, T. Higaki, K. Awai, O. Ikeda, Y. Yamashita. 2020

98 citations recorded by the source corpus.

The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not so complicated. One of the major issues is that commentaries written by experts are difficult to understand, and are not primarily written for clinicians. The purpose of this article was to describe the different concepts behind machine learning, radiomics, and deep learning to make clinicians more familiar with these techniques.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Amina Bolatkan
2. Kazuma Kobayashi
3. Jun Sese
4. Ryuji Hamamoto

Citations in the corpus

• Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “The classification problem is used to predict discrete data, for example, distinguishing between benign and malignant tumors [4].”