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.