.png){kind=icon}
返回
Improving retinal OCT image classification accuracy using medical pre-training and sample replication methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2024.106019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Eye disease is a global health issue involving a large population, and it is necessary to develop high -accuracy diagnostic methods. Manual diagnosis has some challenges, and deep learning for automatic diagnosis is becoming a research focus. However, there are two problems in current deep learning used for medical image tasks: deficient network pre -training on ImageNet dataset consisted of natural scene images, which significantly differ from medical images; a discrepancy between sub -network for training and full network for prediction introduced by dropout. In this study, a medical pre -training method and a sample replication method were proposed to address the problems, respectively. The former involves network pre -training on a large-scale medical image dataset called RadImageNet, followed by transfer learning on a retinal OCT (Optical Coherence Tomography) image dataset. The latter uses JS (Jenson's Shannon) divergence between the two predictions yielded with two identical samples via sample replication as an additional loss function to enforce network's outputs consistency. The two methods were applied on widely used ResNet50, DenseNet121 and InceptionV3 networks, and the effectiveness of using each method and both the two methods to improve the network's performance was demonstrated by experimental results. Compared to those of the corresponding base networks, most the metrics of the networks using the methods were improved for all four sample categories, and the accuracy improvements of 3.76 %, 6.52 % and 8.63 % were obtained for above the networks, respectively. All the networks achieved an accuracy of about 95 % even working with limited target dataset and training resources.
Keyword:
Deep learning
Image classification
Retinal image
OCT image
Automatic disease diagnosis
Eye disease
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
机构
引用论文
分享收藏Crystalline‐State Reaction with Allosteric Effect in Spin‐Crossover, Interpenetrated Networks with Magnetic and Optical Bistability具有磁和光学双稳态的自旋交叉，互穿网络中具有变构效应的晶态反应
PREAI
分享收藏The retina as a window to the brain-from eye research to CNS disorders视网膜作为大脑的窗口-从眼睛研究到中枢神经系统疾病.png)
NATURE REVIEWS NEUROLOGY
NATURE REVIEWS NEUROLOGYIF33.1
PREAI
分享收藏Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?用于自动MRI心脏多结构分割和诊断的深度学习技术: 问题解决了吗？
OAAI
OAAI分享收藏Lösliche
trans
‐Di‐1‐alkinyl‐ und Poly‐
trans
‐ethinyl(tetraalkylphthalocyaninato)metall‐IVB‐DerivateLösliche
trans
-二-1-炔基-与聚-
trans
-乙炔基 (四烷基酞菁) 金属-ⅳ-衍生物
PREAI
分享收藏Monodisperse particle production: A method to prevent drop coalescence using electrostatic forces
PREAI
分享收藏
查看更多内容