Work

Seamless lesion insertion in digital mammography: methodology and reader study

Year: 2016

Type: conference-paper

Source: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

DOI: 10.1117/12.2217056

Authors: Aria Pezeshk, Nicholas Petrick, Berkman Sahiner

Institution: United States Food and Drug Administration

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

OpenAlex ID: W2311041434

FWCI: 0.539

Cites: 10

Cited by: 6

Related to: 10