Name
Abstract | ||
|---|---|---|
Pulmonary nodule differentiation is one of the most challenge tasks of computer-aided diagnosis(CADx). Both texture method and shape estimation approaches previously presented could provide good performance to some extent in the literature. However, no matter 2D or 3D textures extracted, they just tend to observe characteristics of the pulmonary nodules from a statistical perspective according to local features' change, which hints they are helpless to work as global as the human who always be aware of the characteristics of given target as a combination of local features and global features, thus they have certain limitations. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN) and previously contributions provided by texture features, we here presented a hybrid method for better to complete the differentiation task. It can be observed that our proposed multi-channel CNN model has a better discrimination in capacity according to the projection of distributions of extracted features and achieved a new record with AUC 97.04 on LIDC-IDRI database. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67552-7_3 | IMAGING FOR PATIENT-CUSTOMIZED SIMULATIONS AND SYSTEMS FOR POINT-OF-CARE ULTRASOUND |
Keywords | DocType | Volume |
Convolutional neural network, Multi-channel CNN, Texture, CADx, Deeplearning, Pulmonary nodule differentiation | Conference | 10549 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| tingting zhao | 1 | 20 | 6.45 |
| Huafeng Wang | 2 | 59 | 7.87 |
| Lihong Li | 3 | 670 | 45.28 |
| Yifang Qi | 4 | 0 | 0.34 |
| Haoqi Gao | 5 | 0 | 0.68 |
| Fangfang Han | 6 | 29 | 3.70 |
| Zhengrong Liang | 7 | 2 | 2.41 |
| Yanmin Qi | 8 | 0 | 0.34 |
| Yuan Cao | 9 | 19 | 7.21 |