For medical image recognition, deep learning requires a massive training set, while annotation work is a tedious and time-consuming process because of the high technical threshold. Furthermore, it is difficult to guarantee annotation accuracy due to the knowledge, skills, and status of the annotator. In this research, we propose a semi-automated annotation model based on weakly supervised learning. Moreover, a target-level annotation method is proposed based on weakly supervised learning that is guided by machine learning. The machine learning method is used to screen the regions of interest (RoIs), whose semantic feature vectors are extracted by the deep learning method. Then, the machine learning method is used to cluster them, and the RoIs are finally classified and labeled by a distance comparison. Therefore, this model achieves target-level semi-automated annotation by only using image-level annotations. We applied this method to ultrasound imaging of thyroid papillary carcinoma. The experiments demonstrate the potential of this new methodology to reduce the workload for pathologists and increase the objectivity of diagnoses. We find that 89.8% of papillary thyroid carcinoma regions can be detected automatically, while 82.6% of benign and normal tissue can be excluded without the use of any additional immunohistochemical markers or human intervention.

对于医学图像识别，深度学习需要大量的培训，而注释工作由于技术门槛高，是一个繁琐且耗时的过程。此外，由于注释者的知识，技能和状态，难以保证注释的准确性。在这项研究中，我们提出了一种基于弱监督学习的半自动注释模型。此外，基于机器学习指导的弱监督学习，提出了一种目标级注释方法。机器学习方法用于筛选感兴趣区域（RoIs），通过深度学习方法提取其语义特征向量。然后，使用机器学习方法对它们进行聚类，并最终通过距离比较对RoI进行分类和标记。因此，该模型仅通过使用图像级注释即可实现目标级半自动注释。我们将此方法应用于甲状腺乳头状癌的超声成像。实验证明了这种新方法的潜力，可以减少病理学家的工作量并增加诊断的客观性。我们发现，可以自动检测出89.8％的甲状腺乳头状癌区域，而无需使用任何其他免疫组织化学标记或人工干预就可以排除82.6％的良性和正常组织。