Pathway curator: an online webserver extracting genes and interactions from figures

In the biomedical literature, gene pathways are frequently included. Many high-quality gene pathways are illustrated in the form of visuals and text, making them valuable study tools for biological processes and precision medicine. Pathway maps and literature texts provide researchers with access to a huge number of new biological treatments. For general usage, these pathway maps should be logically ordered, coordinated, and converted into a computer-readable format. Currently, keeping up with the rapid increase of the literature requires laborious extraction of information from a publication at a time. A gene pathway map recognition system is devised and implemented in this study. Based on the pathway map and relevant information supplied by users, the system extracts gene identity and gene interaction information, and the automated extraction from pathway maps is efficient. Furthermore, the tool offers users with a full view of a certain illness's pathway, which is useful for researchers and can speed up the research process in a variety of biomedical applications. This thesis first explains the project's goal and provides the background information. The project's design ideas are then presented, as well as an analysis of the system and introductions to related platforms. After that, the system's implementations are described one by one, together with the deployment and testing processes. Finally, potential improvements and future work are discussed.Includes bibliographical references

A Double-Branch Surface Detection System for Armatures in Vibration Motors with Miniature Volume Based on ResNet-101 and FPN

In this paper, a complete system based on computer vision and deep learning is proposed for surface inspection of the armatures in a vibration motor with miniature volume. A device for imaging and positioning was designed in order to obtain the images of the surface of the armatures. The images obtained by the device were divided into a training set and a test set. With continuous experimental exploration and improvement, the most efficient deep-network model was designed. The results show that the model leads to high accuracy on both the training set and the test set. In addition, we proposed a training method to make the network designed by us perform better. To guarantee the quality of the motor, a double-branch discrimination mechanism was also proposed. In order to verify the reliability of the system, experimental verification was conducted on the production line, and a satisfactory discrimination performance was reached. The results indicate that the proposed detection system for the armatures based on computer vision and deep learning is stable and reliable for armature production lines