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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]

Palladium nanoparticles dispersed in ammonium salts of hyperbranched polystyrenes are adhesively immobilized on the surface of cotton or filter papers by simply heating with dicarboxylic acids or halide anions. The resulting Pd@cotton and Pd@filter paper behave as reusable catalysts; in particular, Pd@filter paper is useful as catalyst for application to sequential cross-coupling and hydrogenation reactions to produce several different products with just one piece of the paper catalyst. [ABSTRACT FROM AUTHOR]