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Zhen Cong

2 accepted papers

2020

Unsupervised Content-Preserved Adaptation Network for Classification of Pulmonary Textures from Different CT Scanners

ICASSP 2020accepted

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 cannot perform well when they are directly applied to the data from other scanners. This domain shift problem is caused by diff…

Cited by 0SourceScholar
2018

Pulmonary Textures Classification Using A Deep Neural Network with Appearance and Geometry Cues

ICASSP 2018accepted

Classification of pulmonary textures on CT images is essential for the development of a computer-aided diagnosis system of diffuse lung diseases. In this paper, we propose a novel method to classify pulmonary textures by using a deep neural network, which can make full use of appearance and geometry…

Cited by 0SourceScholar