Abstract
Recently, machine learning-based semantic segmentation algorithms have demonstrated their potential to accurately segment regions and contours in medical images, allowing the precise location of anatomical structures and abnormalities. Although medical images are difficult to acquire and annotate, semi-supervised learning methods are efficient in dealing with the scarcity of labeled data. However, overfitting is almost inevitable due to the limited images for training. Furthermore, the intricate shapes of organs and lesions in medical images introduce additional complexity in different cases, preventing networks from acquiring a strong ability to generalize. To this end, we introduce a novel method called Scaling-up Mix with Multi-Class (SM2C). This method uses three strategies - scaling-up image size, multi-class mixing, and object shape jittering - to improve the ability to learn semantic features within medical images. By diversifying the shape of the segmentation objects and enriching the semantic information within each sample, the SM2C demonstrates its potential, especially in the training of unlabelled data. Extensive experiments demonstrate the effectiveness of the SM2C on three benchmark medical image segmentation datasets. The proposed framework shows significant improvements over state-of-the-art counterparts.
Abstract (translated)
最近，基于机器学习的语义分割算法已经展示了在医学图像中准确分割区域和轮廓的潜力，使得解剖结构和异常情况的准确位置得以确定。尽管获取和标注医学图像具有挑战性，但半监督学习方法在处理数据稀疏的情况方面非常有效。然而，由于训练数据的有限性，过拟合几乎是不可避免的。此外，医学图像中器官和病变形状的复杂性增加了在不同情况下网络泛化能力的问题。为了实现这一目标，我们引入了一种名为“放大混合多类”（SM2C）的新方法。这种方法采用三种策略-放大图像尺寸、多类混合和对象形状扰动-以提高在医学图像中学习语义特征的能力。通过多样化分割对象的形状和丰富每个样本的语义信息，SM2C展示了其潜力，尤其是在未标注数据上的训练。大量实验证明，SM2C在三个医疗图像分割数据集上的效果优于现有方法。所提出的框架在现有技术水平上取得了显著的提高。
URL
https://arxiv.org/abs/2403.16009
