Paper Info
Title
A Preliminary Study Of Predicting Effectiveness Of Anti-Vegf Injection Using Oct Images Based On Deep Learning
Abstract
Deep learning based radiomics have made great progress such as CNN based diagnosis and U-Net based segmentation. However, the prediction of drug effectiveness based on deep learning has fewer studies. Choroidal neovascularization (CNV) and cystoid macular edema (CME) are the diseases often leading to a sudden onset but progressive decline in central vision. And the curative treatment using anti-vascular endothelial growth factor (anti-VEGF) may not be effective for some patients. Therefore, the prediction of the effectiveness of anti-VEGF for patients is important. With the development of Convolutional Neural Networks (CNNs) coupled with transfer learning, medical image classifications have achieved great success. We used a method based on transfer learning to automatically predict the effectiveness of anti-VEGF by Optical Coherence tomography (OCT) images before giving medication. The method consists of image preprocessing, data augmentation and CNN-based transfer learning, the prediction AUC can be over 0.8. We also made a comparison study of using lesion region images and full OCT images on this task. Experiments shows that using the full OCT images can obtain better performance. Different deep neural networks such as AlexNet, VGG-16, GooLeNet and ResNet-50 were compared, and the modified ResNet-50 is more suitable for predicting the effectiveness of anti-VEGF.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176743
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Dehua Feng100.34
Xi Chen233.62
Zhi-Guo Zhou31119.47
Haotian Liu400.34
Yanfen Wang500.34
Ling Bai600.34
Shu Zhang700.34
Xuanqin Mou8155272.38