# RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels

arXiv: https://arxiv.org/abs/2009.12873 · HTML: https://mlsanity.com/paper/2009.12873/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation](https://mlsanity.com/paper/2402.05079/) (2024)
- [Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.10887/) (2024)
- [Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2402.07245/) (2024)
- [When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation](https://mlsanity.com/paper/2208.06449/) (2022)
- [Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion](https://mlsanity.com/paper/2403.17432/) (2024)
- [Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT](https://mlsanity.com/paper/2103.05546/) (2021)
- [Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images](https://mlsanity.com/paper/2104.03715/) (2021)
- [Triple-View Feature Learning for Medical Image Segmentation](https://mlsanity.com/paper/2208.06303/) (2022)
- [Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection](https://mlsanity.com/paper/2301.10441/) (2023)
- [Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation](https://mlsanity.com/paper/2404.01723/) (2024)

## Abstract (arXiv)

Segmentation algorithms for medical images are widely studied for various clinical and research purposes. In this paper, we propose a new and efficient method for medical image segmentation under noisy labels. The method operates under a deep learning paradigm, incorporating four novel contributions. Firstly, a residual interconnection is explored in different scale encoders to transfer gradient information efficiently. Secondly, four copy-and-crop connections are replaced by residual-block-based concatenation to alleviate the disparity between encoders and decoders. Thirdly, convolutional attention modules for feature refinement are studied on all scale decoders. Finally, an adaptive denoising learning strategy (ADL) is introduced into the training process to avoid too much influence from the noisy labels. Experimental results are illustrated on a publicly available benchmark database of spine CTs. Our proposed method achieves competitive performance against other state-of-the-art methods over a variety of different evaluation measures.