The field of medical image Segmentation is hindered by the scarcity of large, publicly available annotated datasets. Not all datasets are made public for privacy reasons, and creating annotations for a large dataset is time-consuming and expensive, as it requires specialized expertise
本研究提出了一种新方法，将Segment Anything Model (SAM)与领域特定知识相结合，可用于构建医学图像分割模型，通过迭代的方式，将无标签图像与SAM和领域特定知识相结合，有效地进行半监督学习，以实现标签效率高的医学图像分割。实验证明该方法在乳腺癌、息肉和皮肤病变分割方面具有有效性，为医学图像分割的半监督学习开辟了新的方向。