Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

Micromachines

Huibin Wang¹  Zhen Zhang¹  Zhe Chen¹  Guanghua Xiao¹  Jie Shen¹  Xiaomin Ge² 

[1] College of Computer and Information Engineering, Hohai University, Nanjing 211100, China;Department of Radiology, Changzhou Second People’s Hospital Affiliated to Nanjing Medical University, Changzhou 213000, China;

关键词: brain tumor classification;    convolutional neural networks;    suppressing strategy;    bilinear pooling;    magnetic resonance imaging;   

DOI  :  10.3390/mi13010015

来源: DOAJ

【 摘 要 】

Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a “dual suppression encoding” block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a “factorized bilinear encoding” layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.

【 授权许可】

Unknown