A New Graph Semi-Supervised Learning Method for Medical Image Automatic Annotation

摘要

In order to solve the problem that the semantic gap cannot be alleviated effectively in the field of medical image retrieval, we propose a new graph semi-supervised learning method for medical image automatic annotation. Because the model adopts semi-supervised technology, it can learn from abundant unlabeled instances to avoid the decreasing of generalization ability which is induced by the relative lack of labeled data. Furthermore, by improving graph based semi-supervised learning technology with normalization and modification of decision boundary on its iterative results, the scoring model effectively reduces the bad impact of asymmetric dataset. In view of the relationship between word extraction and image in image learning model, we analyze image similarity calculation in detail. It effectively combines together into the physician's diagnosis information as high-level semantic feature of image, to calculate the similarity between images more effectively. Finally, the Toy data and clinical data sets gastroscope image sets are conducted with a series of experiments, the results show that the method is superior to traditional image annotation method in this paper.