用图神经网络和自回归滤波实现无监督图像分割，医疗影像效果佳。
UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters
- 基于预训练ViT与图结构建模，结合ARMA滤波捕捉图像拓扑。
- 在ECSSD、KVASIR等数据集上达到当前最优无监督性能。
- 适合医疗图像分割场景，无需标注数据，可媲美有监督方法。
监督分类依赖大量标注数据，推动研究向无监督方法发展，尤其在医疗图像分割等标注困难领域。受视觉变压器（ViT）成功的启发，本文提出一种基于预训练ViT的无监督分割框架。通过挖掘图像内在的图结构，结合自回归移动平均（ARMA）滤波器捕获图拓扑特征，并引入基于模块性的损失函数。同时，在图神经网络中采用缩放指数线性单元（SELU）和SILU激活函数，进一步提升分割性能。该方法在ECSSD、DUTS、CUB等标准图像分割数据集，以及KVASIR、CVC-ClinicDB、ISIC-2018等挑战性医疗图像数据集上均取得当前最优无监督表现，甚至接近有监督方法水平。代码已开源：https://github.com/ksgr5566/UnSeGArmaNet。
原文摘要 · Abstract (English)
The data-hungry approach of supervised classification drives the interest of the researchers toward unsupervised approaches, especially for problems such as medical image segmentation, where labeled data are difficult to get. Motivated by the recent success of Vision transformers (ViT) in various computer vision tasks, we propose an unsupervised segmentation framework with a pre-trained ViT. Moreover, by harnessing the graph structure inherent within the image, the proposed method achieves a notable performance in segmentation, especially in medical images. We further introduce a modularity-based loss function coupled with an Auto-Regressive Moving Average (ARMA) filter to capture the inherent graph topology within the image. Finally, we observe that employing Scaled Exponential Linear Unit (SELU) and SILU (Swish) activation functions within the proposed Graph Neural Network (GNN) architecture enhances the performance of segmentation. The proposed method provides state-of-the-art performance (even comparable to supervised methods) on benchmark image segmentation datasets such as ECSSD, DUTS, and CUB, as well as challenging medical image segmentation datasets such as KVASIR, CVC-ClinicDB, ISIC-2018. The github repository of the code is available on \url{https://github.com/ksgr5566/UnSeGArmaNet}.
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