ESPWA: Deep Learning for Estrogen Receptor Status Prediction in Resource-Limited Settings
Dagoberto Pulido-Arias, Rebecca Henderson, Christophe Millien, Joarly Lomil, Marie Djenane Jose, Gabriel Flambert, Jean Bontemps, Emmanuel Georges, Alekhya Gunturi, Palak Shah, Tiago Goncalves, Jayashree Kalpathy-Cramer, Elizabeth Gerstner, Seth Wander, S. Joe Sirintrapun, Dennis Sgroi, Jose Jeronimo, Philip E. Castle, Kenneth Landgraf, Ali Brown, Temidayo Fadelu, Lawrence N. Shulman, John Guttag, Dan Milner, Jane Brock, Christopher Bridge, Albert Kim
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Obstetrics & Gynecology, University of Alabama-Birmingham, Hopital Universitaire de Mirebalais, Zanmi Lasante, Mirebalais, Haiti, University of Florida College of Medicine, Boston University School of Medicine, Institute of Systems and Computer Engineering, Technology and Science, Faculdade de Engenharia, Universidade do Porto, Department of Ophthalmology, University of Colorado-Anschutz, Massachusetts General Hospital Cancer Center, Department of Pathology, Massachusetts General Hospital, Division of Cancer Prevention, National Cancer Institute, NIH, Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute, NIH, American Society of Clinical Pathology, Center for Global Health, Dana-Farber Cancer Institute, Abramson Cancer Center, University of Pennsylvania, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Department of Pathology, Olive View-UCLA Medical Center