Semantic Scholar Open Access 2018 650 sitasi

Deep learning in medical imaging and radiation therapy.

B. Sahiner Aria Pezeshk Lubomir M. Hadjiiski Xiaosong Wang K. Drukker +3 lainnya

Lihat Sumber DOI

Abstrak

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

Topik & Kata Kunci

Medicine Computer Science

Penulis (8)

B. Sahiner

Aria Pezeshk

Lubomir M. Hadjiiski

Xiaosong Wang

K. Drukker

Kenny H. Cha

R. Summers

M. Giger

Format Sitasi

APA MLA BibTeX

Sahiner, B., Pezeshk, A., Hadjiiski, L.M., Wang, X., Drukker, K., Cha, K.H. et al. (2018). Deep learning in medical imaging and radiation therapy.. https://doi.org/10.1002/mp.13264

Akses Cepat

Lihat di Sumber doi.org/10.1002/mp.13264

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 650×
Sumber Database: Semantic Scholar
DOI: 10.1002/mp.13264
Akses: Open Access ✓