  Colorectal cancer is a leading cause of death worldwide. However, early
diagnosis dramatically increases the chances of survival, for which it is
crucial to identify the tumor in the body. Since its imaging uses
high-resolution techniques, annotating the tumor is time-consuming and requires
particular expertise. Lately, methods built upon Convolutional Neural
Networks(CNNs) have proven to be at par, if not better in many biomedical
segmentation tasks. For the task at hand, we propose another CNN-based
approach, which uses atrous convolutions and residual connections besides the
conventional filters. The training and inference were made using an efficient
patch-based approach, which significantly reduced unnecessary computations. The
proposed AtResUNet was trained on the DigestPath 2019 Challenge dataset for
colorectal cancer segmentation with results having a Dice Coefficient of 0.748.
