title: Invasive ductal breast carcinoma detector that is robust to image magnification in whole digital slides.
abstract: Invasive ductal breast carcinomas (IDBCs) are the most frequent and aggressive subtypes of breast cancer, affecting a large number of Canadian women every year. Part of the diagnostic process includes grading the cancerous tissue at the microscopic level according to the Nottingham modification of the Scarff-Bloom-Richardson system. Although reliable, there exists a growing interest in automating the grading process, which will provide consistent care for all patients. This paper presents a solution for automatically detecting regions expressing IDBC in images of microscopic tissue, or whole digital slides. This represents the first stage in a larger solution designed to automatically grade IDBC. The detector first tessellated whole digital slides, and image features were extracted, such as color information, local binary patterns, and histograms of oriented gradients. These were presented to a random forest classifier, which was trained and tested using a database of 66 cases diagnosed with IDBC. When properly tuned, the detector balanced accuracy, F1 score, and Dice's similarity coefficient were 88.7%, 79.5%, and 0.69, respectively. Overall, the results seemed strong enough to integrate our detector into a larger solution equipped with components that analyze the cancerous tissue at higher magnification, automatically producing the histopathological grade.
authors.affiliation: McGill University, Centre for Intelligent Machines, Electrical and Computer Engineering, 3480 University Street, City, Montreal, Quebec H3A 2A7, Canada; McGill University, Department of Pathology, Henry C. Witelson Laboratory, 1001 Boulevard Decarie, Block E, Montreal, Quebec H4A 3J1, Canada.
authors.forename: Matthew
authors.initials: M
authors.lastname: Balazsi
authors.affiliation: McGill University , Department of Pathology, Henry C. Witelson Laboratory, 1001 Boulevard Decarie, Block E, Montreal, Quebec H4A 3J1, Canada.
authors.forename: Paula
authors.initials: P
authors.lastname: Blanco
authors.affiliation: McGill University , Department of Pathology, Henry C. Witelson Laboratory, 1001 Boulevard Decarie, Block E, Montreal, Quebec H4A 3J1, Canada.
authors.forename: Pablo
authors.initials: P
authors.lastname: Zoroquiain
authors.affiliation: McGill University , Centre for Intelligent Machines, Electrical and Computer Engineering, 3480 University Street, City, Montreal, Quebec H3A 2A7, Canada.
authors.forename: Martin D
authors.initials: MD
authors.lastname: Levine
authors.affiliation: McGill University , Department of Pathology, Henry C. Witelson Laboratory, 1001 Boulevard Decarie, Block E, Montreal, Quebec H4A 3J1, Canada.
authors.forename: Miguel N
authors.initials: MN
authors.lastname: Burnier
keywords: cancer; computer vision; histopathology; invasive ductal breast carcinoma; machine learning; whole digital slides