mlsanityData to 13 Oct 2026

Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation

arXiv 2210.00227 ↗First posted 1 Oct 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

It is a challenge to segment the location and size of rectal cancer tumours through deep learning. In this paper, in order to improve the ability of extracting suffi-cient feature information in rectal tumour segmentation, attention enlarged ConvNeXt UNet (AACN-UNet), is proposed. The network mainly includes two improvements: 1) the encoder stage of UNet is changed to ConvNeXt structure for encoding operation, which can not only integrate multi-scale semantic information on a large scale, but al-so reduce information loss and extract more feature information from CT images; 2) CBAM attention mechanism is added to improve the connection of each feature in channel and space, which is conducive to extracting the effective feature of the target and improving the segmentation accuracy.The experiment with UNet and its variant network shows that AACN-UNet is 0.9% ,1.1% and 1.4% higher than the current best results in P, F1 and Miou.Compared with the training time, the number of parameters in UNet network is less. This shows that our proposed AACN-UNet has achieved ex-cellent results in CT image segmentation of rectal cancer.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 1 Oct 2022.

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17 of 24 references.

References · 17

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5 more
  1. 2018Dual Attention Network for Scene Segmentationcited by 1,002
  2. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
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  5. 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71

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