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- 摘要
- 关键词
- 实验方案
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Deep Learning-Based Automated Classification of Multi-Categorical Abnormalities From Optical Coherence Tomography Images
摘要： Purpose: To develop a new intelligent system based on deep learning for automatically optical coherence tomography (OCT) images categorization. Methods: A total of 60,407 OCT images were labeled by 17 licensed retinal experts and 25,134 images were included. One hundred one-layer convolutional neural networks (ResNet) were trained for the categorization. We applied 10-fold cross-validation method to train and optimize our algorithms. The area under the receiver operating characteristic curve (AUC), accuracy and kappa value were calculated to evaluate the performance of the intelligent system in categorizing OCT images. We also compared the performance of the system with results obtained by two experts. Results: The intelligent system achieved an AUC of 0.984 with an accuracy of 0.959 in detecting macular hole, cystoid macular edema, epiretinal membrane, and serous macular detachment. Specifically, the accuracies in discriminating normal images, cystoid macular edema, serous macular detachment, epiretinal membrane, and macular hole were 0.973, 0.848, 0.947, 0.957, and 0.978, respectively. The system had a kappa value of 0.929, while the two physicians’ kappa values were 0.882 and 0.889 independently. Conclusions: This deep learning-based system is able to automatically detect and differentiate various OCT images with excellent accuracy. Moreover, the performance of the system is at a level comparable to or better than that of human experts. This study is a promising step in revolutionizing current disease diagnostic pattern and has the potential to generate a significant clinical impact.
关键词： artificial intelligence,deep learning,optical coherence tomography,ResNet
更新于2025-09-23 15:22:29
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11259 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV) || Conductive Particles Detection in the TFT-LCD Manufacturing Process with U-ResNet
摘要： The inspection of conductive particles after Anisotropic Conductive Film (ACF) bonding is a common and crucial step in the TFT-LCD manufacturing process since quality of conductive particles is an indicator of ACF bonding quality. Manual inspection under microscope is a time consuming and tedious work. There is a demand in industry for automatic conductive particle inspection system. The challenge of automatic conductive particle quality inspection is the complex background noise and diversified particle appearance, including shape, size, clustering and overlapping etc. As a result, there lacks effective automatic detection method to handle all the complex particle patterns. In this paper, we propose a U-shaped deep residual neural network (U-ResNet), which can learn features of particle from massive labeled data. The experimental results show that the proposed method achieves high accuracy and recall rate, which exceedingly outperforms the previous work.
关键词： Deep convolutional network,Conductive particles,TFT-LCD,U-ResNet
更新于2025-09-23 15:21:01