2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)最新文献

Wia-Spine: A CBIR environment with embedded radiomic features to assess fragility fractures Wia-Spine:一种具有嵌入式放射学特征的CBIR环境来评估脆性骨折

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00020

M. Bedo, Jonathan S. Ramos, A. J. Traina, C. Traina, M. Nogueira-Barbosa, P. M. A. Marques

{"title":"Wia-Spine: A CBIR environment with embedded radiomic features to assess fragility fractures","authors":"M. Bedo, Jonathan S. Ramos, A. J. Traina, C. Traina, M. Nogueira-Barbosa, P. M. A. Marques","doi":"10.1109/CBMS55023.2022.00020","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00020","url":null,"abstract":"Osteoporosis is a systemic disorder that reduces the bone mineral density, increasing the vertebrae's fragility and proneness to fracture. Although the bone densitometry index t-Score is a solid marker for the osteoporosis diagnosis, its measure alone is insufficient to predict the future development of fragility fractures. A complementary approach to address vertebral bone characterization is the analysis of magnetic resonance imaging (MRI) by radiomic features, which model vertebral bodies' morphological properties after color and texture. Radiomic features have been employed for detecting fragility fractures in related work, but, to the best of our knowledge, no study has been conducted on their suitability to recover similar, diagnosed cases that could hint at future fractures. We fulfill this gap by designing a Content-based Image Retrieval (CBIR) tool with embedded radiomic features, which uses past cases recovered from an annotated database to (i) identify an existing fragility fracture in a query vertebra and (ii) predict a fracture to a query vertebra from an aging patient. The proposed CBIR was evaluated on a reference database of 273 vertebral bodies from sagittal T2-weighted MRIs. The results indicate our fine-tuned approach spotted fragility fractures accurately $(mathrm{F}1-text{Score} =0.83, text{Precision} =0.83, text{AUC} =0.81, text{CI} =95%)$. We also investigated the CBIR potential to predict fractures in a case study regarding three patients from the reference database (confirmed osteoporosis, MRI in [2012–2017]). The system correctly inferred the prediction of future fractures for query vertebrae, which were confirmed a few years later (MRI in [2018–2021]). Such empirical findings suggest CBIR can support a differential diagnosis in the assessment of local fragility fractures.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"182 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123290857","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}

引用次数: 0

End-to-End Multi-task Learning Regression Network for Fovea Localization in Fundus Images 眼底图像中央凹定位的端到端多任务学习回归网络

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00076

Limin Huang, Haijun Lei, Weixing Liu, Z. Li, Hai Xie, Baiying Lei

{"title":"End-to-End Multi-task Learning Regression Network for Fovea Localization in Fundus Images","authors":"Limin Huang, Haijun Lei, Weixing Liu, Z. Li, Hai Xie, Baiying Lei","doi":"10.1109/CBMS55023.2022.00076","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00076","url":null,"abstract":"Macular fovea localization in fundus images is a critical stage for computer-aided diagnostic techniques of many retinal diseases. Due to its cluttered visual characteristics, it is difficult to accurately locate the fovea. Many previous methods obtain the location of macular fovea from pre-extracting image features extracted from surrounding structures, such as optic disc and vascular distribution. Deep learning-based regression techniques are promising due to their effective modeling of the relationship between the fovea and its surrounding structure for fovea localization. However, there are still many challenges to locate the fovea using deep learning accurately. To address these issues, we design a novel end-to-end multi-task learning regression network for fovea localization. Specifically, the proposed network consists of two regression networks. For the coordinate regression network, we introduce multi-scale fusion technology and a multi-head self-attention module to extract discriminative context information and capture long-term dependence, respectively. For the heatmap regression network, the generated heatmap according to the coordinates is utilized to supervise the output of the network. The experimental results on three public datasets demonstrate that our method achieves superior performance for the localization of macular fovea.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115590004","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}

引用次数: 1

Breast Cancer Diagnosis from Histopathology Images using Supervised Algorithms 使用监督算法从组织病理学图像中诊断乳腺癌

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00025

Alberto Labrada, B. Barkana

引用次数: 4

Generative Adversarial Networks for Augmenting EEG Data in P300-based Applications: A Comparative Study 基于p300的脑电数据增强生成对抗网络的比较研究

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00038

Yasmin Abdelghaffar, Ahmed Hashem, S. Eldawlatly

{"title":"Generative Adversarial Networks for Augmenting EEG Data in P300-based Applications: A Comparative Study","authors":"Yasmin Abdelghaffar, Ahmed Hashem, S. Eldawlatly","doi":"10.1109/CBMS55023.2022.00038","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00038","url":null,"abstract":"The performance of P300-based Brain-Computer Interface (BCI) applications is highly dependent on both the quality and quantity of the recorded Electroencephalography (EEG) signals. As recording extended datasets from users for calibration is often a difficult and tedious task, data augmentation can be used to help supplement the training data for machine learning classifiers that are typically used in P300-based BCI applications. In this paper, we analyze and compare the performance of three different generative adversarial networks (GANs) as data augmentation techniques; namely, deep convolutional GAN (DCGAN), conditional GAN (cGAN), and the auxiliary classifier GAN (ACGAN). We first investigated the effect of increasing the training data size using each of these GANs on the performance of P300 classification. Our results revealed that the cGAN increased the classification accuracy by up to 18% relative to the baseline data under the best conditions. We also investigated the effect of decreasing the training data size and compensating for the reduced data size using data generated from the GANs. Our analysis indicated that the training data size could be reduced by ~30% while maintaining the accuracy on par with the baseline accuracy. These results demonstrate the utility of GANs in addressing the challenges associated with the limited data typically available for BCI applications.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121929450","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}

引用次数: 1

Evaluation of Relevance-Driven Compression of Regular Cataract Surgery Videos 常规白内障手术视频的相关性驱动压缩评价

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00083

Natalia Mathá, Klaus Schoeffmann, S. Sarny, Doris Putzgruber-Adamitsch, Y. El-Shabrawi

引用次数: 1

Ensemble Framework for Unsupervised Cervical Cell Segmentation 无监督子宫颈细胞分割的集成框架

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00068

Agnimitra Sen, Shyamali Mitra, S. Chakraborty, Debashri Mondal, K. Santosh, N. Das

引用次数: 2

Ultrasonic Carotid Blood Flow Velocimetry Based on Deep Complex Neural Network 基于深度复杂神经网络的超声颈动脉血流速度测量

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00032

Jian Lei, Xun Lang, Bingbing He, Songhua Liu, Hao Tan, Yufeng Zhang

{"title":"Ultrasonic Carotid Blood Flow Velocimetry Based on Deep Complex Neural Network","authors":"Jian Lei, Xun Lang, Bingbing He, Songhua Liu, Hao Tan, Yufeng Zhang","doi":"10.1109/CBMS55023.2022.00032","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00032","url":null,"abstract":"Precise measurement of carotid artery blood flow is of vital importance for studying thrombosis and early carotid atherosclerotic plaque. However, the traditional non-parametric methods are limited by the weak detection ability to low-velocity blood flow, and show problems including the large measurement deviation and long algorithm running time. Motivated by the above status quo, a novel method based on deep complex convolutional neural network (DCCNN) is proposed for carotid blood flow velocimetry. Based on supervised learning, DCCNN feeds the echo signals into complex convolutional layers for the purpose of rejecting clutter signals. Then, the outputs of complex convolutional layers are processed by the complex fully connected layers to estimate the blood flow velocity. The effectiveness of the proposed method is verified by simulation as well as in vivo data of healthy volunteers. Compared with typical velocimetry methods such as the high-pass filter and singular value decomposition, the normalized root mean square error (NRMSE) of the velocimetry result obtained from the proposed method is reduced by 47.20%) and 45.45%, and the goodness-of-fit is improved by 5.64%, 3.36%, respectively. In addition, the running time of DCCNN is reduced by 82.10% and 21.11%, respectively. Such results show that the proposed method is a promising tool for blood flow velocity measurement due to its higher velocity measurement accuracy and good real-time performance.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"50 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006000","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}

引用次数: 1

Deep Learning Based Multi-Label Prediction of Hospitalization for COVID-19 Cases 基于深度学习的COVID-19住院病例多标签预测

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00024

C. Leung, Thanh Huy Daniel Mai, N. D. Tran

{"title":"Deep Learning Based Multi-Label Prediction of Hospitalization for COVID-19 Cases","authors":"C. Leung, Thanh Huy Daniel Mai, N. D. Tran","doi":"10.1109/CBMS55023.2022.00024","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00024","url":null,"abstract":"Health informatics is an interdisciplinary area where computer science and related disciplines meet to address problems and support healthcare and medicine. In particular, computer has played an important role in medicine. Many existing computer-based systems (e.g., machine learning models) for healthcare applications produce binary prediction (e.g., whether a patient catches a disease or not). However, there are situations in which a non-binary prediction (e.g., what is hospitalization status of a patient) is needed. As a concrete example, over the past two years, people around the world have been affected by the coronavirus disease 2019 (COVID-19) pandemic. There have been works on binary prediction to determine whether a patient is COVID-19 positive or not. With availability of alternative methods (e.g., rapid test), such a binary prediction has become less important. Moreover, with the evolution of the disease (e.g., recent development of COVID-19 Omicron variant), multi-label prediction of the hospitalization status has become more important when compared with binary prediction on the confirmation of cases. Hence, in this paper, we present a multi-label prediction system for computer-based medical applications. Our system makes use of autoencoders (consisting of encoders and decoders) and few-shot learning to predict the hospitalization status (e.g., ICU, semi-ICU, regular wards, or no hospitalization). The prediction is important for allocation of medical resources (e.g., hospital facilities and medical staff), which in turn affect patient lives. Experimental results on real-life open datasets show that, when training with only a few data, our multilabel prediction system gave a high F1-score when predicting hospitalization status of COVID-19 cases.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124489225","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}

引用次数: 2

Fine-grained Encryption for Secure Research Data Sharing 细粒度加密安全研究数据共享

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00089

L. Reis, M. T. D. Oliveira, S. Olabarriaga

{"title":"Fine-grained Encryption for Secure Research Data Sharing","authors":"L. Reis, M. T. D. Oliveira, S. Olabarriaga","doi":"10.1109/CBMS55023.2022.00089","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00089","url":null,"abstract":"Research data sharing requires provision of adequate security. The requirements for data privacy are extremely demanding for medical data that is reused for research purposes. To address these requirements, the research institutions must implement adequate security measurements, and this demands large effort and costs to do it properly. The usage of adequate access controls and data encryption are key approaches to effectively protect research data confidentiality; however, the management of the encryption keys is challenging. There are novel mechanisms that can be explored for managing access to the encryption keys and encrypted files. These mechanisms guarantee that data are accessed by authorised users and that auditing is possible. In this paper we explore these mechanisms to implement a secure research medical data sharing system. In the proposed system, the research data are stored on a secure cloud system. The data are partitioned into subsets, each one encrypted with a unique key. After the authorisation process, researchers are given rights to use one or more of the keys and to selectively access and decrypt parts of the dataset. Our proposed solution offers automated fine-grain access control to research data, saving time and work usually made manually. Moreover, it maximises and fortifies users' trust in data sharing through secure clouds solutions. We present an initial evaluation and conclude with a discussion about the limitations, open research questions and future work around this challenging topic.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133087295","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}

引用次数: 0

Explanations of Deep Networks on EEG Data via Interpretable Approaches 基于可解释方法的深度网络对EEG数据的解释

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00037

Chen Cui, Y. Zhang, Shenghua Zhong

引用次数: 0