Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

• Published in: Scientific reports Vol. 7; no. 1; p. 46450
• Main Authors: Cruz-Roa, Angel, Gilmore, Hannah, Basavanhally, Ajay, Feldman, Michael, Ganesan, Shridar, Shih, Natalie N.C., Tomaszewski, John, González, Fabio A., Madabhushi, Anant
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.04.2017; Nature Publishing Group
• Subjects: 13/56; 631/114/1305; 631/67/1347; 639/166/985; 639/705/117; 692/700/1421; Adult; Aged; Algorithms; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Carcinoma, Ductal, Breast - diagnostic imaging; Carcinoma, Ductal, Breast - pathology; Deep Learning; Digitization; Female; Genomes; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Invasiveness; Mammography; Middle Aged; multidisciplinary; Neoplasm Invasiveness; Neural networks; Pathology; Scanners; Science; Tumor Burden; Tumors; Young Adult

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent...