mlsanityData to 7 Oct 2026

Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet

arXiv 2103.09289 ↗First posted 16 Mar 2021eess.IV cs.CV cs.LGcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

14 of 25 references.

References · 14

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2 more
  1. 2020OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Imagescited by 10
  2. 2020Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNetcited by 6

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1.

2 papers.

  1. 2021Structure-aware scale-adaptive networks for cancer segmentation in whole-slide imagescited by 0
  2. 2023Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imagingcited by 0