Lymphocyte Detection in Hematoxylin-Eosin Stained Histopathological Images of Breast Cancer

Abstract

Lymphocytes are immune cells that form an important bio-marker in the prognosis of breast cancer. In some cases more e ective treatment can be chosen based on the lymphocyte presence near tumor regions. For trained pathologists the detection of lymphocytes in Hematoxylin-Eosin stained images is however a challenging and time intensive task with subjective interpretations. In this research we explore the lymphocyte detection problem with a deep learning approach and strive towards a robust, objective and e cient tool for computer aided diagnosis. We generate a large data-set with machine produced labels by applying an existing model on destained and restained immunohistochemical histopathological images. On this data we train and evaluate a more minimal rendition of the known YOLO object detection model and report moderate results.