Name
Title | ||
|---|---|---|
Residual Convolutional Neural Networks With Global And Local Pathways For Classification Of Focal Liver Lesions |
Abstract | ||
|---|---|---|
Computer-aided diagnosis ( CAD) systems are useful in assisting radiologists with clinical diagnoses by classifying focal liver lesions based on computed tomography ( CT) images. Extracting discriminative features from CT images is a crucial step in CAD systems. Although deep neural networks have demonstrated immense success in the computer vision community, there still remain two challenges in this field of medical image analysis. First, there are only limited dataset. Second, the local and global information of lesions both are necessary for this task. In this study, inspired by the importance of global and local information, we propose a novel model for distinguishing diverse types of focal liver lesions, called residual CNN with global and local pathways ( ResNet-GL) model. The model uses both patches of lesion region ( local information) and a whole-lesion region ( global information) as inputs. Since the proposed ResNet-GL is a pixel-wise classification method ( it assigns a label to each pixel of the lesion), we used the normalized label map produced by the ResNet-GL as an input of support vector machines ( SVM) for classification of lesions. The effectiveness of our model is confirmed by the accuracy of our testing dataset, which is over 87%. Our results show that the proposed ResNet-GL model can not only achieves superior performance than state-of-the-art approaches, which are based on mid-level learning-based features, but also can achieve results better than other CNN-based approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-97304-3_47 | PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I |
Keywords | Field | DocType |
Deep learning, Liver lesions classification, Local information, Global information, Computer-aid diagnosis (CAD) | CAD,Residual,Pattern recognition,Computer science,Convolutional neural network,Support vector machine,Artificial intelligence,Pixel,Deep learning,Discriminative model,Machine learning,Medical diagnosis | Conference |
Volume | ISSN | Citations |
11012 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Dong | 1 | 326 | 52.32 |
| Lanfen Lin | 2 | 78 | 24.70 |
| Hongjie Hu | 3 | 11 | 9.50 |
| Qiaowei Zhang | 4 | 6 | 2.85 |
| Qingqing Chen | 5 | 6 | 3.86 |
| Yutaro lwamoto | 6 | 4 | 0.79 |
| Xian-Hua Han | 7 | 14 | 10.19 |
| Yen-Wei Chen | 8 | 720 | 155.73 |