Paper Info
Title
FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification
Abstract
Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans. However, it is challenging due to uneven brightness, location variability, inter-class similarity, and intraclass dissimilarity, affecting the classification accuracy. To address the above issues, we propose a Fourier-based Frequency Complex Network (FFCNet) for colon disease classification in this study. Specifically, FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning to overcome the loss of phase information caused by real convolution operations. Also, our Fourier transform transfers the average brightness of an image to a point in the spectrum (the DC component), alleviating the effects of uneven brightness by decoupling image content and brightness. Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local disease-specific features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 colonoscopy images, showing our method achieves high performance outperforming previous state-of-the-art methods with an accuracy of 86.35% and an accuracy of 4.46% higher than the backbone.
Year
DOI
Venue
2022
10.1007/978-3-031-16437-8_8
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT III
Keywords
DocType
Volume
Colon disease classification, Frequency learning, Complex convolutional network
Conference
13433
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Kai-Ni Wang100.34
Yuting He200.34
Shuaishuai Zhuang300.34
Juzheng Miao400.68
Xiaopu He500.34
Ping Zhou600.34
Guanyu Yang700.34
Guang-Quan Zhou800.34
Shuo Li901.01