Author Archives: wj.shin

Efficient neural network compression via transfer learning for machine vision inspection

10월 21, 2021

[ Abstract ]

Several practical difficulties arise when trying to apply deep learning to image-based industrial inspection tasks: training datasets are difficult to obtain, each image must be inspected in milliseconds, and defects must be detected with 99% or greater accuracy. In this paper we show how, for image-based industrial inspection tasks, transfer learning can be leveraged to address these challenges. Whereas transfer learning is known to work well only when the source and target domain images are similar, we show that using ImageNet—whose images differ significantly from our target industrial domain—as the source domain, and performing transfer learning, works remarkably well. For one benchmark problem involving 5,520 training images, the resulting transfer-learned network achieves 99.90% accuracy, compared to only a 70.87% accuracy achieved by the same network trained from scratch. Further analysis reveals that the transfer-learned network produces a considerably more sparse and disentangled representation compared to the trained-from-scratch network. The sparsity can be exploited to compress the transfer-learned network up to 1/128 the original number of convolution filters with only a 0.48% drop in accuracy, compared to a drop of nearly 5% when compressing a trained-from-scratch network. Our findings are validated by extensive systematic experiments and empirical analysis.

Age-group determination of living individuals using first molar images based on artificial intelligence

1월 21, 2021

[ Abstract ]

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.