Using masks to improve classification performance of convolutional neural networks with applications to cancer-cell screening

Abstract

In cancer-cell screening, a patient's cells are classified by a convolutional neural network (CNN) to identify abnormal cells. In one approach, a mask having a center more transparent than the mask's periphery is used to mask an input image containing a cell of interest to yield a masked image. Since the cell is usually located around an image center, and since the image often contains irrelevant objects, such as normal cells and micro-organisms, around an image periphery, interference due to the irrelevant objects in training the CNN and in classification is diminished by using the masked image rather than the original one. In another approach, masking is applied to feature maps before classification. In the CNN, this masking is accomplished by convolving each feature map with a convolutional kernel to produce an intermediate feature map followed by chopping off a peripheral region thereof to yield a downsized feature map.