Context Aware Lung Cancer Annotation in Whole Slide Images Using Fully Convolutional Neural Networks

We propose a novel machine learning based methodology for detection and annotation of areas in Whole Slide lung Images (WSI) that are affected by lung cancer. Contrary to the trend of processing WSIs in small overlapping patches to generate a heat-map, we use a much larger patch with no overlap, aiming at capturing more of the context in each patch. As these larger patches are less likely to completely fall into one of the cancer/co-cancer classes, we use a pixel-level image segmentation approach consisting of a custom Fully Convolutional Neural Networks (FCNN). As opposed to the trend of using very deep neural networks, we carefully design a small FCNN, while avoiding the trainable upsampling layers, in order to cope with small training data and inaccurate region-based labeling of WSIs. We show that such an efficient architecture achieves better accuracy compared to the heat-map based approach. Apart from the descent results of our small network, this study shows that FCNNs are capable of learning region-based human labeling of biomedical images that sometimes does not correspond to a texture or a bounded object as a whole, but is more like drawing a line around a region containing a scattered number of small malignant tissues.