Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical images. In the VOC dataset experiments, we used the code from the BAM project on GitHub as a benchmark and compared it. Additionally, we improved the benchmark code by adding the Attention Mechanism (CBAM), placing its position before the support set undergoes global average pooling. By comparing the experimental results, we found that the model with the added CBAM achieved better segmentation results under small sample conditions. In the esophageal medical image experiments, the code from the BAM project was also used as a benchmark and a similar experimental design was performed. We further added CBAM by placing it before the global average pooling of the support set. The experimental results show that the model with the addition of CBAM achieves a significant improvement in the small sample segmentation task of esophageal medical images. To summarize, this study validates the effectiveness of using the BAM model in the small-sample medical image segmentation task on two different datasets. By adding CBAM, our model exhibits better segmentation performance under small sample conditions. These findings provide useful insights for small-sample medical image segmentation and are important for future research and practice.

中文翻译：

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使用 CBAM 改进小样本医学图像分割：来自两个数据集的见解

医学图像分割是医学领域的关键任务之一，对于准确的病灶检测和治疗计划至关重要。然而，小样本问题一直是医学图像分割的挑战之一。在本研究中，基于两个不同的数据集（VOC 数据集和食管医学图像）对小样本医学图像分割问题进行了实验评估。在VOC数据集实验中，我们使用GitHub上BAM项目的代码作为基准并进行比较。此外，我们通过添加注意力机制（CBAM）来改进基准代码，将其位置放置在支持集进行全局平均池化之前。通过对比实验结果，我们发现加入CBAM的模型在小样本条件下取得了更好的分割效果。在食管医学图像实验中，也以BAM项目的代码为基准，进行了类似的实验设计。我们进一步添加了 CBAM，将其放置在支持集的全局平均池之前。实验结果表明，加入CBAM的模型在食管医学图像小样本分割任务上取得了显着的提升。总而言之，本研究验证了在两个不同数据集上使用 BAM 模型进行小样本医学图像分割任务的有效性。通过添加 CBAM，我们的模型在小样本条件下表现出更好的分割性能。这些发现为小样本医学图像分割提供了有用的见解，对未来的研究和实践很重要。