  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.
