no code implementations • 29 Nov 2023 • Lukas Hirsch, Yu Huang, Hernan A. Makse, Danny F. Martinez, Mary Hughes, Sarah Eskreis-Winkler, Katja Pinker, Elizabeth Morris, Lucas C. Parra, Elizabeth J. Sutton
Reevaluating these regions in 10% of all cases with higher AI-predicted risk could have resulted in up to 33% early detections by a radiologist.
no code implementations • 21 Sep 2020 • Lukas Hirsch, Yu Huang, Shaojun Luo, Carolina Rossi Saccarelli, Roberto Lo Gullo, Isaac Daimiel Naranjo, Almir G. V. Bitencourt, Natsuko Onishi, Eun Sook Ko, Doris Leithner, Daly Avendano, Sarah Eskreis-Winkler, Mary Hughes, Danny F. Martinez, Katja Pinker, Krishna Juluru, Amin E. El-Rowmeim, Pierre Elnajjar, Elizabeth A. Morris, Hernan A. Makse, Lucas C Parra, Elizabeth J. Sutton
Conclusion: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.