Lymphocytes are immune cells that form an important bio-marker in the prognosis of breast cancer. In some cases more effective 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 efficient 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.