Deep neural networks have been widely used in medical image analysis and
medical image segmentation is one of the most important tasks. U-shaped neural
networks with encoder-decoder are prevailing and have succeeded greatly in
various segmentation tasks. While CNNs treat an image as a grid of pixels in
Euclidean space and Transformers recognize an image as a sequence of patches,
graph-based representation is more generalized and can construct connections
for each part of an image. In this paper, we propose a novel ViG-UNet, a graph
neural network-based U-shaped architecture with the encoder, the decoder, the
bottleneck, and skip connections. The downsampling and upsampling modules are
also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and
Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most
existing classic and state-of-the-art U-shaped networks.

该论文提出了一种基于图神经网络的 U 形结构体系 ViG-UNet，在多个分割任务中表现良好。

ViG-UNet：面向医学图像分割的视觉图神经网络

ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation

In recent years, "U-shaped" neural networks featuring encoder and decoder
structures have gained popularity in the field of medical image segmentation.
Various variants of this model have been developed. Nevertheless, the
evaluation of these models has received less attention compared to model
development. In response, we propose a comprehensive method for evaluating
medical image segmentation models for multi-indicator and multi-organ (named
MIMO). MIMO allows models to generate independent thresholds which are then
combined with multi-indicator evaluation and confidence estimation to screen
and measure each organ. As a result, MIMO offers detailed information on the
segmentation of each organ in each sample, thereby aiding developers in
analyzing and improving the model. Additionally, MIMO can produce concise
usability and comprehensiveness scores for different models. Models with higher
scores are deemed to be excellent models, which is convenient for clinical
evaluation. Our research tests eight different medical image segmentation
models on two abdominal multi-organ datasets and evaluates them from four
perspectives: correctness, confidence estimation, Usable Region and MIMO.
Furthermore, robustness experiments are tested. Experimental results
demonstrate that MIMO offers novel insights into multi-indicator and
multi-organ medical image evaluation and provides a specific and concise
measure for the usability and comprehensiveness of the model. Code:
this https URL

本研究提出了一种全面的评估医学图像分割模型的方法，名为 MIMO，它允许模型生成独立的阈值，然后与多指标评估和置信度估计相结合，对每个器官进行筛选和测量。实验结果表明，MIMO 为多指标和多器官医学图像评估提供了新的见解，并为模型的可用性和全面性提供了明确的度量。