WSAN: An Effective Model of Weakly Supervised Similarity Analysis Network for the Lung CT Images
Yi Zhuang, Nan Jiang, Shuai Chen · IEEE Access · 2022
With the rapid development of medical imaging technologies, the high-resolution CT image data is of great value for medical research as well as a clinical diagnosis. The paper takes lung CT image as an example. Retrieving images similar to the input one is helpful to assist physicians in clinical diagnosis. Compared with the traditional content-based image retrieval, the similarity retrieval of lung CT images requires higher retrieval accuracy, with similar requirements in external shape as well as internal vascular and lesion location similarity. In the state-of-the-art supervised deep learning networks, the learning of the network relies on labeling. The labeling of medical images, however, requires time and effort for the professionals to label each image, which is too costly. In this paper, we propose a weakly supervised deep learning network model for similarity analysis of lung CT images that is called a Weakly Supervised similarity Analysis Network(WSAN). Extensive experiments show that the WSAN model achieves satisfactory results in measuring the similarity between lung CT images and can be used for similarity retrieval tasks.