Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

By J. Burt, N. Torosdagli, Naji Khosravan, Harish RaviPrakash, Aliasghar Mortazi, F. Tissavirasingham, Sarfaraz Hussein, Ulas Bagci. 2018

147 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Florian Tramèr
3. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
• Artificial Intelligence in Health Care: Bibliometric Analysis (2020, unknown): “These techniques have been widely adopted by oncology research to predict disease and survival rates [42].”