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.
@article{arxiv.2103.09289,
title = {Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet},
author = {Nisarg A. Shah and Divij Gupta and Romil Lodaya and Ujjwal Baid and Sanjay Talbar},
journal= {arXiv preprint arXiv:2103.09289},
year = {2021}
}
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5th IAPR International Conference on Computer Vision and Image Processing, 12 pages