Ensemble Of Neural Networks For High Endothelial Venules Detection In Meca-79 Immunohistochemistry Images

The presence of high endothelial venules (HEVs) in the tumor micro-environment is associated with favorable clinical out-come in breast cancer and other human solid tumors. HEVs can be highlighted in tissue samples by MECA-79 immunohistochemistry. HEVs are heterogeneously scattered in the tumor micro-environment, preferentially in areas infiltrated by lymphocytes, and their density must be assessed at high resolution on whole slide in the tumor area. We present a frame-work for HEV detection through classification of MECA-79 immunostaining in whole slide images. A classifier is provided, as well as a whole slide image metric for general performance evaluation of detectors. An original ensemble strategy for convolutional neural networks has been developed and its relevance in MECA-79 and general immunostaining classification tasks is discussed.