  Medical image segmentation is a critical step in computer-aided diagnosis,
and convolutional neural networks are popular segmentation networks nowadays.
However, the inherent local operation characteristics make it difficult to
focus on the global contextual information of lesions with different positions,
shapes, and sizes. Semi-supervised learning can be used to learn from both
labeled and unlabeled samples, alleviating the burden of manual labeling.
However, obtaining a large number of unlabeled images in medical scenarios
remains challenging. To address these issues, we propose a Multi-level Global
Context Cross-consistency (MGCC) framework that uses images generated by a
Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning.
The framework involves of two stages. In the first stage, a LDM is used to
generate synthetic medical images, which reduces the workload of data
annotation and addresses privacy concerns associated with collecting medical
data. In the second stage, varying levels of global context noise perturbation
are added to the input of the auxiliary decoder, and output consistency is
maintained between decoders to improve the representation ability. Experiments
conducted on open-source breast ultrasound and private thyroid ultrasound
datasets demonstrate the effectiveness of our framework in bridging the
probability distribution and the semantic representation of the medical image.
Our approach enables the effective transfer of probability distribution
knowledge to the segmentation network, resulting in improved segmentation
accuracy. The code is available at
https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency.
