Name
Abstract | ||
|---|---|---|
To diagnose various autoimmune diseases, the accurate Human Epithelial-2 (HEp-2) cell image classification is a very important step. Automatic classification of HEp-2 cell using microscope image is a highly challenging task due to the strong illumination changes derived from the low contrast of the cells. To address this challenge, we propose a deep residual network (ResNet) based framework to recognize HEp-2 cell automatically. Specifically, a residual network of 50 layers (ResNet-50) with substantial deep layer is adopted to acquire the informative feature for accurate recognition. To further boost the recognition performance, we devise a novel ResNet-based network with deep supervision. The deeply supervised ResNet (DSRN) can address the optimization problem of gradient vanishing/exploding and accelerate the convergence speed. DSRN can directly guide the training of the lower and upper levels of the network to counteract the effects of unstable gradient variations by the adverse training process. As a result, DSRN can extract more discriminative features. Experimental results show that our proposed DSRN method can achieve an average classification accuracy of 93.46% and 95.88% on ICPR2012 and ICPR2016-Task1 datasets, respectively. Our proposed method outperforms the traditional methods as well. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545751 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Keywords | Field | DocType |
HEp-2 cell classification, Deep residual network, Deeply supervised ResNet (DSRN) | Convergence (routing),Residual,Computer vision,Task analysis,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Residual neural network,Contextual image classification,Discriminative model,Optimization problem | Conference |
ISSN | Citations | PageRank |
1051-4651 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai Xie | 1 | 9 | 5.30 |
| Yejun He | 2 | 6 | 2.18 |
| Haijun Lei | 3 | 107 | 15.30 |
| Tao Han | 4 | 57 | 11.82 |
| Zhen Yu | 5 | 37 | 4.31 |
| Bai Ying Lei | 6 | 119 | 24.99 |