Name
Title | ||
|---|---|---|
Fibroatheroma Identification In Intravascular Optical Coherence Tomography Images Using Deep Features |
Abstract | ||
|---|---|---|
Identifying vulnerable plaque is important in coronary heart disease diagnosis. Recent emerged imaging modality, Intravascular Optical Coherence Tomography (IVOCT), has been proved to be able to characterize the appearance of vulnerable plaques. Comparing with the manual method, automated fibroatheroma identification would be more efficient and objective. Deep convolutional neural networks have been adopted in many medical image analysis tasks. In this paper, we introduce deep features to resolve fibroatheroma identification problem. Deep features which extracted using four deep convolutional neural networks, AlexNet, GoogLeNet, VGG-16 and VGG-19, are studied. And a dataset of 360 IVOCT images from 18 pullbacks are constructed to evaluate these features. Within these 360 images, 180 images are normal IVOCT images and the rest 180 images are IVOCT images with fibroatheroma. Here, one pullback belongs to one patient; leave-one-patient-out cross-validation is employed for evaluation. Data augmentation is applied on training set for each classification scheme. Linear support vector machine is conducted to classify the normal IVOCT image and IVOCT image with fibroatheroma. The experimental results show that deep features could achieve relatively high accuracy in fibroatheroma identification. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037120 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Training set,Computer vision,Optical coherence tomography,Computer science,Convolutional neural network,Support vector machine,Classification scheme,Artificial intelligence | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 1 | 0.34 |
References | Authors | |
5 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengdi Xu | 1 | 3 | 1.38 |
| Jun Cheng | 2 | 214 | 20.65 |
| Annan Li | 3 | 222 | 14.22 |
| Jimmy Addison Lee | 4 | 20 | 5.57 |
| Damon Wing Kee Wong | 5 | 434 | 37.78 |
| Akira Taruya | 6 | 1 | 0.34 |
| Tanaka, A. | 7 | 1 | 0.68 |
| N. Foin | 8 | 1 | 1.02 |
| P. Wong | 9 | 1 | 0.68 |