Deep network based methods have been proposed for accurate classification of pulmonary textures on CT images. However, such methods well-trained on CT data from one scanner can- not perform well when they are directly applied to the data from other scanners. This domain shift problem is caused by different physical components and scanning protocols of different CT scanners. In this paper, we propose an unsu- pervised content-preserved adaptation network to address this problem. Our method can make a previously well-trained deep network to be adapted for the data of a new CT scan- ner and does not require the laboring annotation to delineate pulmonary texture regions on the new CT data. Extensive evaluations have been carried on images collected from GE and Toshiba CT scanners and show that the proposed method can alleviate the performance degradation problem of classifying pulmonary textures from different CT scanners.

[1] Rui Xu, Zhen Cong, Xinchen Ye*, Shoji Kido, Noriyuki Tomiyama, Unsupervised Content-Perserved Adaptation Network for Classification of Pulmonary Textures from Different CT Scanners, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Virtual Barcelona, May 4-8 2020. (CCF-B)