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The amount of labeled samples always plays a significant role in deep learning. When there are insufficient training samples, deep learning usually struggles to carry out its task successfully. The problem becomes more prominent in that labeled data are expensive to obtain. Therefore, we want to address the problem by introducing a method that utilizes unlabeled samples to improve object detection performance. We propose a framework that utilizes a pseudo‐labeling approach in semi‐supervised learning and applies it to disease diagnosis of chest X‐ray images. Our objective is to train a model that works as a labeling expert and perform the labeling task for improving the object (disease area) detection model by these newly labeled samples. In addition, we also apply an ensemble technique to our framework to reduce the model bias and improve the generalization ability for pseudo‐labeling, which results in improvements in object detection performance. Our proposed method improves the mean average precision up to 5.32, compared with the method without the pseudo‐labeling. Additionally, the mean average precision further increases by up to 19.11 when applying ensemble learning. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a new fish bone detection technique using a convolutional neural network (CNN) and synthetic image generation. Semantic segmentation CNNs with supervised learning generally require a large number of training images and their pixel‐wise teaching signals. In fish bone detection, there are two problems with using semantic segmentation CNNs. One is the manual annotations of fish bones and the other is the difficulty of sampling all variations of fish bones with various lengths, angles, and thicknesses. The proposed method, however, generates them by drawing virtual fish bones on X‐ray images. This technique is very useful for reducing the cost of collecting and annotating a dataset. Experimental results have shown that the average F‐measure for the proposed method is 0.747, while that for a normal training method is 0.493. In the proposed method, the CNN successfully detected actual fish bones despite its training only with virtual fish bones. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]