2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)最新文献_第7页

Learning Pre- and Post-contrast Representation for Breast Cancer Segmentation in DCE-MRI 学习DCE-MRI中乳腺癌分割的对比前后表征

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00070

Hong Wu, Yingwen Huo, Yupeng Pan, Zeyan Xu, Rian Huang, Yu Xie, Chu Han, Zaiyi Liu, Yi Wang

{"title":"Learning Pre- and Post-contrast Representation for Breast Cancer Segmentation in DCE-MRI","authors":"Hong Wu, Yingwen Huo, Yupeng Pan, Zeyan Xu, Rian Huang, Yu Xie, Chu Han, Zaiyi Liu, Yi Wang","doi":"10.1109/CBMS55023.2022.00070","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00070","url":null,"abstract":"Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a considerable role in high-risk breast cancer diagnosis and image-based prognostic prediction. The accurate and robust segmentation of cancerous regions is with clinical demands. However, automatic segmentation remains challenging, due to the large variations of cancers in shape and size, and the class-imbalance issue. To tackle these problems, we offer a two-stage framework, which leverages both pre- and post-contrast images for the segmentation of breast cancer. Specifically, we first employ a breast segmentation network, which generates the breast region of interest (ROI) thus removing confounding information from thorax region in DCE-MRI. Furthermore, based on the generated breast ROI, we offer an attention network to learn both pre- and post-contrast representations for distinguishing cancerous regions from the normal breast tissue. The efficacy of our framework is evaluated on a collected dataset of 261 patients with biopsy-proven breast cancers. Experimental results demonstrate our method attains a Dice coefficient of 91.11% for breast cancer segmentation. The proposed framework provides an effective cancer segmentation solution for breast examination using DCE-MRI. The code is publicly available at https://github.com/2313595986/BreastCancerMRI.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 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":"125859457","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

A novel CNN model with dense connectivity and attention mechanism for arrhythmia classification 一种具有密集连接和注意机制的心律失常分类CNN新模型

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00016

Qin Zhan, Peilin Li, Yongle Wu, Jingchun Huang, Xunde Dong

引用次数: 1

Optimizing Nozzle Travel Time in Proton Therapy 质子治疗中喷嘴行程时间优化

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00085

M. Spezialetti, Renata Di Filippo, Ramon Gimenez De Lorenzo, G. Gravina, G. Placidi, Guido Proietti, F. Rossi, S. Smriglio, J. M. R. Tavares, F. Vittorini, F. Mignosi

{"title":"Optimizing Nozzle Travel Time in Proton Therapy","authors":"M. Spezialetti, Renata Di Filippo, Ramon Gimenez De Lorenzo, G. Gravina, G. Placidi, Guido Proietti, F. Rossi, S. Smriglio, J. M. R. Tavares, F. Vittorini, F. Mignosi","doi":"10.1109/CBMS55023.2022.00085","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00085","url":null,"abstract":"Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"5 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":"123178908","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

TSEUnet: A 3D neural network with fused Transformer and SE-Attention for brain tumor segmentation TSEUnet:一种融合Transformer和SE-Attention的三维神经网络用于脑肿瘤分割

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00030

Yan-Min Chen, Jiajun Wang

引用次数: 1

Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image 基于鼻内镜图像对比学习的腺样体肥大分级网络

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00074

Siting Zheng, Xuechen Li, Mingmin Bi, Yuxuan Wang, Haiyan Liu, Xia Feng, Yunping Fan, Linlin Shen

{"title":"Contrastive learning-based Adenoid Hypertrophy Grading Network Using Nasoendoscopic Image","authors":"Siting Zheng, Xuechen Li, Mingmin Bi, Yuxuan Wang, Haiyan Liu, Xia Feng, Yunping Fan, Linlin Shen","doi":"10.1109/CBMS55023.2022.00074","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00074","url":null,"abstract":"Adenoid hypertrophy is a common disease in children with otolaryngology diseases. Otolaryngologists usually use nasoendoscopy for adenoid hypertrophy screening, which is however tedious and time-consuming for the grading. So far, artificial intelligence technology has not been applied to the grading of nasoendoscopic adenoid. In this work, we firstly propose a novel multi-scale grading network, MIB-ANet, for adenoid hypertrophy classification. And we further propose a contrastive learning-based network to alleviate the overfitting problem of the model caused by lacking of nasoendoscopic adenoid images with high-quality annotations. The experimental results show that MIB-ANet shows the best grading performance compared to four classic CNNs, i.e., AlexNet, VGG16, ResNet50 and GoogleNet. Take $F_{1}$ score as an example, MIB-ANet achieves 1.38% higher $F_{1}$ score than the best baseline CNN - AlexNet. Due to the capability of the contrastive learning-based pre-training strategy in exploring unannotated data, the pre-training using SimCLR pretext task can consistently improve the performance of MIB-ANet when different ratios of the labeled training data are employed. The MIB-ANet pre-trained by SimCLR pretext task achieves 4.41%, 2.64%, 3.10%, and 1.71% higher $F_{1}$ score when 25%, 50%, 75% and 100% of the training data are labeled, respectively.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"8 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":"132361442","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 Lesions Segmentation using Dual-level UNet (DL-UNet) 基于双级UNet的乳腺病变分割

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00067

Yanjiao Zhao, Zhihui Lai, Linlin Shen, Heng Kong

引用次数: 2

Ethically Informed Software Process for Smart Health Home 智能健康家庭的伦理知情软件流程

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00040

Xiang Zhang, M. Pike, Nasser Mustafa, V. Brusic

引用次数: 1

Classification of cardiac cohorts based on morphological and hemodynamic features derived from 4D PC-MRI data 基于4D PC-MRI数据的形态学和血流动力学特征的心脏队列分类

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00081

Uli Niemann, Atrayee Neog, B. Behrendt, K. Lawonn, M. Gutberlet, M. Spiliopoulou, B. Preim, M. Meuschke

{"title":"Classification of cardiac cohorts based on morphological and hemodynamic features derived from 4D PC-MRI data","authors":"Uli Niemann, Atrayee Neog, B. Behrendt, K. Lawonn, M. Gutberlet, M. Spiliopoulou, B. Preim, M. Meuschke","doi":"10.1109/CBMS55023.2022.00081","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00081","url":null,"abstract":"An accurate assessment of the cardiovascular system and prediction of cardiovascular diseases (CVDs) are crucial. Cardiac blood flow data provide insights about patient-specific hemodynamics. However, there is a lack of machine learning approaches for a feature-based classification of heart-healthy people and patients with CVDs. In this paper, we investigate the potential of morphological and hemodynamic features extracted from measured blood flow data in the aorta to classify heart-healthy volunteers (HHV) and patients with bicuspid aortic valve (BAV). Furthermore, we determine features that distinguish male vs. female patients and elderly HHV vs. BAV patients. We propose a data analysis pipeline for cardiac status classification, encompassing feature selection, model training, and hyperparameter tuning. Our results suggest substantial differences in flow features of the aorta between HHV and BAV patients. The excellent performance of the classifiers separating between elderly HHV and BAV patients indicates that aging is not associated with pathological morphology and hemodynamics. Our models represent a first step towards automated diagnosis of CVS using interpretable machine learning models.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"68 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":"116164233","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

Data Augmentation Methods For Object Detection and Segmentation In Ultrasound Scans: An Empirical Comparative Study 超声扫描中目标检测与分割的数据增强方法:实证比较研究

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00057

Sachintha R. Brandigampala, Abdullah F. Al-Battal, Truong Q. Nguyen

{"title":"Data Augmentation Methods For Object Detection and Segmentation In Ultrasound Scans: An Empirical Comparative Study","authors":"Sachintha R. Brandigampala, Abdullah F. Al-Battal, Truong Q. Nguyen","doi":"10.1109/CBMS55023.2022.00057","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00057","url":null,"abstract":"In ultrasound imaging, sonographers are tasked with analyzing scans for diagnostic purposes; a challenging task, especially for novice sonographers. Deep Learning methods have shown great potential in their ability to infer semantics and key information from scans to assist with these tasks. However, deep learning methods require large training sets to accomplish tasks such as segmentation and object detection. Generating these large datasets is a significant challenge in the medical domain due to the high cost of acquisition and annotation. Therefore, data augmentation is used to increase the size of training datasets to create the needed variability for deep learning models to generalize. These augmentation methods try to mimic differences among scans that result from noise, tissue movement, acquisition settings, and others. In this paper, we analyze the effectiveness of general augmentation methods that perform color, rigid, and non-rigid geometric transformation, to empirically analyze and compare their ability to improve the performance of three segmentation architectures on three different ultrasound datasets. We observe that non-rigid geometric transformations produce the best performance improvement.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"16 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":"117325041","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

A YOLO-based Object Simplification Approach for Visual Prostheses 基于yolo的视觉假体对象简化方法

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00039

Reham H. Elnabawy, Slim Abdennadher, O. Hellwich, S. Eldawlatly

{"title":"A YOLO-based Object Simplification Approach for Visual Prostheses","authors":"Reham H. Elnabawy, Slim Abdennadher, O. Hellwich, S. Eldawlatly","doi":"10.1109/CBMS55023.2022.00039","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00039","url":null,"abstract":"Visual prostheses have been introduced to partially restore vision to the blind via visual pathway stimulation. Despite their success, some challenges have been reported by the implanted patients. One of those challenges is the difficulty of object recognition due to the low resolution of the images perceived through these devices. In this paper, a deep learning-based approach combined with image pre-processing is proposed to allow visual prostheses' users to recognize objects in a given scene. The approach simplifies the objects in the scene by displaying the objects in clip art form to enhance object recognition. These clip art images are generated by, first, identifying the objects in the scene using the You Only Look Once (YOLO) deep neural network. The clip art corresponding to each identified object is then retrieved via Google Images. Three experiments were conducted to measure the success of the proposed approach using simulated prosthetic vision. Our results reveal a remarkable decrease in the recognition time, increase in the recognition accuracy and confidence level when using the clip art representation as opposed to using the actual images of the objects. These results demonstrate the utility of object simplification in enhancing the perception of images in prosthetic vision.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"4 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":"116922419","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