  During the last decade, the digitization of pathology has gained considerable
momentum. Digital pathology offers many advantages including more efficient
workflows, easier collaboration as well as a powerful venue for telepathology.
At the same time, applying Computer-Aided Diagnosis (CAD) on Whole Slide Images
(WSIs) has received substantial attention as a direct result of the
digitization. The first step in any image analysis is to extract the tissue.
Hence, background removal is an essential prerequisite for efficient and
accurate results for many algorithms. In spite of the obvious discrimination
for human operators, the identification of tissue regions in WSIs could be
challenging for computers, mainly due to the existence of color variations and
artifacts. Moreover, some cases such as alveolar tissue types, fatty tissues,
and tissues with poor staining are difficult to detect. In this paper, we
perform experiments on U-Net architecture with different network backbones
(different topologies) to remove the background as well as artifacts from WSIs
in order to extract the tissue regions. We compare a wide range of backbone
networks including MobileNet, VGG16, EfficientNet-B3, ResNet50, ResNext101 and
DenseNet121. We trained and evaluated the network on a manually labeled subset
of The Cancer Genome Atlas (TCGA) Dataset. EfficientNet-B3 and MobileNet by
almost 99% sensitivity and specificity reached the best results.
