Work

Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions

Year: 2023

Type: article

Source: Modern Pathology

DOI: 10.1016/j.modpat.2023.100369

Authors: Shahd A. Alajaji, Zaid H. Khoury, Mohamed Elgharib, Mamoon Saeed, Ahmed R.H. Ahmed, Mohammad B. Khan, Tiffany Tavares, Maryam Jessri, Adam C. Puche, Hamid Hoorfar, Ivan Stojanov, James J. Sciubba, Ahmed S. Sultan

Institutions: University of Maryland, Baltimore, King Saud University, Meharry Medical College, Max Planck Institute for Informatics, AstraZeneca (United States), The University of Texas at San Antonio Health Science Center, Queensland Health, The University of Queensland, Metro North Hospital and Health Service, Cleveland Clinic, Johns Hopkins University, University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: University of Maryland, Baltimore

OpenAlex ID: W4387955776

FWCI: 4.98

Cites: 87

Cited by: 57

Related to: 10