# Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet

arXiv: https://arxiv.org/abs/2103.09289 · HTML: https://mlsanity.com/paper/2103.09289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Towards Robust Neural Networks via Random Self-ensemble](https://mlsanity.com/paper/1712.00673/) (2017)
- [A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis](https://mlsanity.com/paper/2001.00258/) (2020)
- [RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans](https://mlsanity.com/paper/1811.01328/) (2018)
- [Signet Ring Cell Detection With a Semi-supervised Learning Framework](https://mlsanity.com/paper/1907.03954/) (2019)
- [OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images](https://mlsanity.com/paper/2004.12668/) (2020)
- [Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet](https://mlsanity.com/paper/2006.15954/) (2020)

## Built on by (most cited first, 2 of 2)

- [Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images](https://mlsanity.com/paper/2109.12617/) (2021)
- [Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging](https://mlsanity.com/paper/2306.11137/) (2023)

## Abstract (arXiv)

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.