Proceedings of the 6th International Conference on Biomedical Signal and Image Processing最新文献

{"title":"Transmission Model of Tuberculosis Disease with Age Structure of Human in Thailand","authors":"P. Pongsumpun","doi":"10.1145/3484424.3484428","DOIUrl":"https://doi.org/10.1145/3484424.3484428","url":null,"abstract":"Mycobacterium tuberculosis caused Tuberculosis. This bacteria attack the lungs and the other parts of the body. Tuberculosis distributed from human to human through the air. The symptoms of Tuberculosis in the lungs may include a bad cough that lasts >= 3 weeks, weight loss, fever and night sweats .The tuberculosis(TB) is appeared in about one-quarter of the world's population. TB cases appeared in different age group. In this research, we formulate the model can explain the spread of TB with age group. We separated human into 3 age groups and each human group is classified unto 5 groups such as susceptible, exposed, infectious, quarantined and recovered. The analyzation and simulation of our model are shown.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122210736","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":"Crossmodal Matching Transformer based X-ray and CT image registration for TEVAR","authors":"Meng Li, Changyan Lin, Lixia Shu, Xin Pu, Yu Chen, Heng Wu, Jiasong Li, Hongshuai Cao","doi":"10.1145/3484424.3484438","DOIUrl":"https://doi.org/10.1145/3484424.3484438","url":null,"abstract":"Since the mapping relationship between definitized intra-interventional 2D X-ray and undefined pre-interventional 3D Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, a cross-modal matching transformer network is proposed to matching 2D X-ray and 3D CT images directly. The proposed approach first learns skeletal features from 2D X-ray and 3D CT images. The features are then converted into 1D X-ray and CT representation vectors, which are combined using a transformer module. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary 2D X-ray and 3D CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133968722","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":"Genotype Imputation with Homomorphic Encryption","authors":"Fook Mun Chan, Ahmad Al Badawi, Jun Jie Sim, B. Tan, Foo Chuan Sheng, Khin Mi Mi Aung","doi":"10.1145/3484424.3484426","DOIUrl":"https://doi.org/10.1145/3484424.3484426","url":null,"abstract":"Genotype imputation is a technique used to determine unobserved genomic markers when sequencing genomic data. This is a cost effective method for sequencing a genome. Due to the large amount of personal identifiable information involved in genomic imputation, there is a rising concern for analysis of such nature to be secure and private. We describe a method using homomorphic encryption (HE) to perform genotype imputation in a secure and private setting. Our solution first involves training a logistic regression model and performing the imputation in the encrypted domain. We have implemented our solution over using the open sourced Homomorphic Encryption library, SEAL. We are able to impute 500 SNPs within 5 minutes, with an accuracy of 97.3%.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442330","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":"Unified Framework to Construct Fast Row-Action-Type Iterative CT Reconstruction Methods with Total Variation Using Multi Proximal Splitting","authors":"Heejeong Kim, Kazuya Sadakata, H. Kudo","doi":"10.1145/3484424.3484435","DOIUrl":"https://doi.org/10.1145/3484424.3484435","url":null,"abstract":"Recently, to realize low-dose scan in CT imaging, a number of iterative reconstruction methods with Total Variation (TV) regularization have been investigated. In particular, reconstruction methods based on proximal splitting framework have been actively researched thanks to the following two reasons. First, they allow to use the TV regularization term, which is known to be a non-differentiable function that cannot be handled with classical optimization methods. Second, using the proximal splitting leads to a new class of iterative reconstruction methods which cannot be found in the literature. The major drawback of existing research in this direction is that most of them use the proximal splitting which divides the cost function into a sum of only two terms like famous Chambolle-Pock algorithm and FISTA. In this paper, we propose a unified framework to construct a class of row-action-type iterative methods which converge very fast using frameworks of multi proximal splitting which divides the cost function into a sum of arbitrary number of sub-cost functions (more than two). The use of multi proximal splitting naturally allows us to construct row-action-type iterative methods converging to an exact minimizer of the cost function very quickly. In mathematical literature, there exist only three different frameworks of multi proximal splitting, which are the Passty splitting, Dykstra-like splitting, and modified Dykstra-like splitting. We develop three new iterative methods, i.e. the Passty iterative method, Dykstra-like iterative method, and modified Dykstra-like iterative method, by using these frameworks, for the case where the cost function is a sum of the standard least-squares data fidelity and the TV regularization term. We have compared the proposed three iterative methods with an empirical standard method using ordered-subset technique called OS-SIRT-TV method. The results demonstrate that the performances of proposed methods significantly outperform OS-SIRT method in terms of image quality with a comparable convergence speed.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113022","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":"A Liver Electrosurgery Simulator Developed by Unity Engine","authors":"Hongjie Zeng, Xuejun Zhang, Jingxian Chen, Yini Wei, Deyu Kong, Xianfu Xu","doi":"10.1145/3484424.3484427","DOIUrl":"https://doi.org/10.1145/3484424.3484427","url":null,"abstract":"Virtual surgery has been widely used in medical training for safety and cost reasons. The hepatectomy simulator we proposed in this paper is a virtual surgical system designed to simulate the resection of liver with electric knife. It can help medical students familiarize themselves with surgical procedures and improve their skills in repeatable training. Liver model used in this study was reconstructed from real CT images, position-based dynamics has been used to make the model deformable. By combining VR headset with force feedback device, a more realistic operating environment is presented. According to the evaluation results, users all have a positive attitude towards the usefulness and usability of the simulator. Existing problems of the simulator have been discussed in this paper, as well as directions for further research.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117201602","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":"Retracted August 7, 2023: Uniqueness Results of Mixed Interior and Exterior Problems for CT Image Reconstruction","authors":"Katsuya Fujii, Yongchae Kim, H. Kudo","doi":"10.1145/3484424.3484433","DOIUrl":"https://doi.org/10.1145/3484424.3484433","url":null,"abstract":"Retraction Notice: It has been determined that this paper was submitted without the consent and knowledge of one of the authors. As a result, ACM is retracting the paper. As from the date of the retraction, August 7, 2023, this paper should not be cited as a publication in the literature and the authors have been advised to no longer list this Work as a published paper in their official list of publications. The names of individual co-author(s) who did not provide consent to have the paper submitted with their name who were originally listed on this published paper have been removed from the published version of record with a new corrected version of record (CVoR) published on this site, an updated version of the front matter for the ICBIP'21 conference citation page, an updated list of ICBIP'21 papers, and an updated author list on this citation page. The original published Version of Record will be hidden in the ACM Digital Library, but can be made available upon request in order to comply with governmental legal requirements or as part of an official legal investigation. For further information, contact the ACM Director of Publications.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121800227","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":"An FPGA-Based Hardware Accelerator for 2D Labeling","authors":"Xianfu Xu, Wenjun Su, Bin Li, Yini Wei, Deyu Kong, Hongjie Zeng, Xuejun Zhang","doi":"10.1145/3484424.3484436","DOIUrl":"https://doi.org/10.1145/3484424.3484436","url":null,"abstract":"The 2D Labeling algorithm is used in many applications due to its superior image processing quality. As the requirements of image processing continue to increase, generating huge computational workloads, the efficient real-time implementation of this algorithm is very challenging. In recent years, research on accelerating 2D Labeling algorithms on GPUs has made rapid progress. However, GPU devices usually bring a large amount of energy consumption and are therefore not suitable for a wide range of applications in embedded scenarios. In this paper, we propose a highly integrated general-purpose hardware accelerator for medical image processing to effectively improve the computational performance of 2D Labeling algorithms and reduce the power consumption of FPGA devices. The design integrates image denoising, edge detection, and image segmentation algorithms in a hardware IP core based on a deep pipelining framework, which can effectively improve the speed of 2D Labeling algorithm during intensive medical image processing through parallel computing and data reuse. The design is implemented on Xilinx ZYNQ XC7Z020, and we consume very less energy and improve the computational performance by 1.3 and 2.1 times, respectively, compared to the software design based on advanced NVIDIA GeForce GTX 1660 Super and Intel(R) Core (TM) i7-10700 CPUs.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122251202","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":"Molecular and Mechanical Pathways in Airway Smooth Muscle Relaxation","authors":"Lulu Wang, A. Al-Jumaily, Oleksiy Kuleshov, S. Ponomarenko, E. Khutoryan","doi":"10.1145/3484424.3484429","DOIUrl":"https://doi.org/10.1145/3484424.3484429","url":null,"abstract":"Chemistry and physics play an important role in relaxing contracted airway smooth muscles (ASM). This work investigates whether combining the two will result in enhanced ASM relaxation. Isolated bovine trachea ASM strips were employed using acetylcholine before undergoing mechanically‐induced length oscillations, consisting of small oscillations superimposed on breathing‐like changes, followed by administration of a broncho‐relaxant agent. The agents used in this work are isoproterenol (ISO), Indomethacin (INDO), SB 203580, or PD 0980590, which are inhibitors of cyclooxygenase extracellular signal‐regulated kinase, 1/2 (ERK1/2), and p38 mitogen‐activated protein kinase, respectively. The ASM reactivity following these treatments is assessed. It is demonstrated that adding one of these agents, Indomethacin, ISO, or SB 203580 enhances the relaxation produced by the oscillation process while it does not work with PD0980590.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124123309","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":"Analysis of the Mathematical Model of Covid-19 in Thailand","authors":"P. Pongsumpun","doi":"10.1145/3484424.3484425","DOIUrl":"https://doi.org/10.1145/3484424.3484425","url":null,"abstract":"The purpose of this research is to study the characteristics of the COVID-19 virus in Thailand. We formulate the mathematical model of COVID-19 virus. We separate the human populations into 6 groups. The infected human populations are separated into 2 classes such as infectious human population with no show symptom and infectious human population with symptoms. We study the behavior of the equilibrium points of the model. Determine the conditions for the local stability of the equilibrium points. Numerical results of mathematical models are presented. This will lead to a reduction in the mortality rate of patients in Thailand.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130179327","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":"Trans-Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images","authors":"A. Alharbi, Yaqi Wang, Qianni Zhang","doi":"10.1145/3484424.3484437","DOIUrl":"https://doi.org/10.1145/3484424.3484437","url":null,"abstract":"The detection of cancerous tissue in histopathological slides is of great value in both clinical practice and pathology research. This paper presents a novel approach that targets automatically classifying cancer tissue by leveraging an attention multiple instance learning scheme; an attention-equivalent neural network-based permutation-invariant aggregation operator applied on the multi-instance learning network. Additionally, we propose a Trans-AMIL approach which is designed to apply Transfer Learning pre-trained models and learn the distribution of the bag label probability using neural networks. We demonstrate experimentally that our approach outperforms several conventional deep learning-based methods on an open BreakHis cancer histopathology dataset.","PeriodicalId":225954,"journal":{"name":"Proceedings of the 6th International Conference on Biomedical Signal and Image Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124827305","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}