{"title":"A Deeper Understanding of Modular DNN in Predicting Ageing-Related Disease","authors":"Xiaosong Yuan, Ruoyang Hong, Danyating Shen","doi":"10.1145/3506651.3506659","DOIUrl":"https://doi.org/10.1145/3506651.3506659","url":null,"abstract":"Ageing is a significant process happening in all humans and close related to health and lifetime. However, the mechanism of ageing is poorly understood. Getting to know about which specific genes control ageing-related diseases can be a great help of this mechanism. This paper focuses on using one of the most advanced machine learning methods nowadays to predict ageing related disease with large amount of genes. This paper finds a deeper relation behind the different datasets and encoders of modular DNN raised by Fabio Fabris’ group. With a deeper understanding of modular DNN, this paper is able to find a model with AUC value equal to 0.9732, which has a 10.65% improvement compared with former paper. With the results and final model of this paper, this paper can help scientists identify high-possible ageing-related genes with higher accuracy.","PeriodicalId":280080,"journal":{"name":"2021 4th International Conference on Digital Medicine and Image Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116701311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research and Design of Neonatal Jaundice Detector Based on Color Sensor","authors":"Chao Wu, Liang Huang, Weiwei Han, Wei-Ning Liang, Xinfu Jiang, Zebin Huang, Qing-Yun Deng","doi":"10.1145/3506651.3506664","DOIUrl":"https://doi.org/10.1145/3506651.3506664","url":null,"abstract":"Objective: To solve the problem of invasive detection of neonatal jaundice and prone to infection. Methods: A neonatal jaundice detector based on color sensing was proposed. The microprocessor controls the color sensor to read the RGB color components of the skin reflection, and performs a multiple linear regression model fitting with the serum total bilirubin to calculate the corresponding neonatal jaundice index. Results: The paired T test with JM-103 shows that the measurement results have good correlation. The Bland-Altman method was used in neonatal measurement to analyze and verify its consistency. Conclusion: The clinical comparison and verification show that the monitor can measure neonatal jaundice index, and the design has good applicability.","PeriodicalId":280080,"journal":{"name":"2021 4th International Conference on Digital Medicine and Image Processing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123276275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep Monochromatic Metal Artifact Reduction for Computed Tomography","authors":"Sally Sijie Song","doi":"10.1145/3506651.3506653","DOIUrl":"https://doi.org/10.1145/3506651.3506653","url":null,"abstract":"Computed tomography (CT) is a three-dimensional medical imaging modality that uses X-ray beams to generate cross-sectional images of the human anatomy. Although CT is widely used in medical diagnosis, the presence of metal implants often severely impair the diagnostic value of CT images. The presence of metal implants causes errors in the image that are called “metal artifacts”. Existing metal artifact reduction (MAR) algorithms are either ineffective or require a large training dataset which is difficult to attain due to the inaccessibility of clinical data. Thus, this study proposes a novel end-to-end convolutional neural network with autoencoder embeddings for MAR that overcomes the shortcomings of existing methods. Unlike existing methods that simulate training data using artificially synthesized metal implant shapes, our research proposes a new data synthesis technique that uses randomly generated polygons to automate the data simulation process. Experimental results prove that this method drastically improves the efficiency of the data generation process. Our proposed network also significantly outperforms state-of-the-art MAR techniques, achieving an MSE < 7 × 10− 6, an SSIM index > 0.994, and a PSNR > 58 dB on a simulated training dataset of 130 samples.","PeriodicalId":280080,"journal":{"name":"2021 4th International Conference on Digital Medicine and Image Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125152790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving Face Recognition for Mask Wearers Using Data Augmentation of Left–Right Image Flipping and Rotation","authors":"M. Hongo, T. Goto","doi":"10.1145/3506651.3506661","DOIUrl":"https://doi.org/10.1145/3506651.3506661","url":null,"abstract":"Wearing a mask hides half of a face, making it difficult to recognize using face recognition. This raises the problem of it being impossible to identify the whereabouts of a person because their face cannot be recognized. In this paper, we aim to improve the recognition rate by using a learning-based method combining an NVIDIA pre-trained model and face images with and without masks. Furthermore, we aim to improve the recognition rate by utilizing data augmentation to increase the number of training data. Experimental results show that the recognition rate of face images improved.","PeriodicalId":280080,"journal":{"name":"2021 4th International Conference on Digital Medicine and Image Processing","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}