Year: 2025
Type: conference-paper
Abstract: Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked.This lack of attention may harm the generalizability of algorithms and, consequently, negatively impact patient outcomes.While existing medical imaging literature reviews mostly focus on machine
Authors: Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer, Víctor M. Campello, Aasa Feragen, Enzo Ferrante, Melanie Ganz, Judy Wawira Gichoya, Camila Gonzalez, Steff Groefsema, Alessa Hering, Adam Hulman, Leo Joskowicz, Dovile Juodelyte, Melih Kandemir, Thijs Kooi, Jorge del Pozo Lérida, Livie Yumeng Li, Andre Pacheco, Tim Rädsch, Mauricio Reyes, Théo Sourget, Bram van Ginneken, David Wen, Nina Weng, Jack Junchi Xu, Hubert Dariusz Zajaç, Maria A. Zuluaga, Veronika Cheplygina
Institutions: University of Copenhagen, Radboud University Medical Center, Universitat de Barcelona, Technical University of Denmark, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad de Buenos Aires, Institute of Astronomy and Space Physics, Rigshospitalet, Emory University, Stanford University, University of Groningen, Aarhus University, Hebrew University of Jerusalem, University of Southern Denmark, Lunit (South Korea), Universidade Federal do Espírito Santo, Radboud University Nijmegen, University of Oxford, Oxford University Hospitals NHS Trust, Copenhagen University Hospital, EURECOM
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4411542006