Deep learning with convolutional neural network in radiology

By Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Shigeru Kiryu, Osamu Abe. 2018

346 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, 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. Gilmer Valdes
2. Philippe Lambin
3. Timothy D. Solberg
4. V. Kearney

In Biomedical Engineering

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Philippe Lambin
2. Arvid Lundervold

Citations in the corpus

• A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, background): “Given enough high-quality data (text and images), it is expected that the role of CNNs will continue to expand in medicine and quantitative imaging (75-80).”
• Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “Recent AI, developed to utilize images [14, 111], and radiologists are particularly well matched.”