论文标题
RAR-U-NET：通过残留连接框架在嘈杂标签下进行脊柱分割的剩余编码器编码
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels
论文作者
论文摘要
针对医学图像的分割算法进行了广泛的研究，以用于各种临床和研究目的。在本文中，我们提出了一种在嘈杂标签下进行医学图像分割的新方法。该方法在深度学习范式下运行，并结合了四个新颖的贡献。首先，在不同的规模编码器中探索了残留的互连，以有效地传输梯度信息。其次，四个副本连接被基于残留块的串联取代，以减轻编码器和解码器之间的差异。第三，在所有刻度解码器上研究了用于特征细化的卷积注意模块。最后，将适应性的脱氧学习策略（ADL）引入到培训过程中，以避免嘈杂标签过多的影响。在脊柱CTS的公开基准数据库中说明了实验结果。我们提出的方法在各种不同的评估措施中针对其他最先进的方法实现了竞争性能。
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.
