ScribbleVC: Scribble-supervised Medical Image Segmentation with
  Vision-Class Embedding
Zihan Li Yuan Zheng Xiangde Luo Dandan Shan Qingqi Hong
Medical image segmentation plays a critical role in clinical decision-making,
treatment planning, and disease monitoring. However, accurate segmentation of
medical images is challenging due to several factors, such as the lack of
high-quality annotation, imaging noise, and anatomical differences across
patients. In addition, there is still a considerable gap in performance between
the existing label-efficient methods and fully-supervised methods. To address
the above challenges, we propose ScribbleVC, a novel framework for
scribble-supervised medical image segmentation that leverages vision and class
embeddings via the multimodal information enhancement mechanism. In addition,
ScribbleVC uniformly utilizes the CNN features and Transformer features to
achieve better visual feature extraction. The proposed method combines a
scribble-based approach with a segmentation network and a class-embedding
module to produce accurate segmentation masks. We evaluate ScribbleVC on three
benchmark datasets and compare it with state-of-the-art methods. The
experimental results demonstrate that our method outperforms existing
approaches in terms of accuracy, robustness, and efficiency. The datasets and
code are released on GitHub.