2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)最新文献_第8页

Penalized Entropy: a novel loss function for uncertainty estimation and optimization in medical image classification 惩罚熵:一种新的用于医学图像分类不确定性估计和优化的损失函数

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00061

Dehua Feng, Xi Chen, Xiaoyu Wang, Jiahuan Lv, Lin Bai, Shu Zhang, Zhiguo Zhou

{"title":"Penalized Entropy: a novel loss function for uncertainty estimation and optimization in medical image classification","authors":"Dehua Feng, Xi Chen, Xiaoyu Wang, Jiahuan Lv, Lin Bai, Shu Zhang, Zhiguo Zhou","doi":"10.1109/CBMS55023.2022.00061","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00061","url":null,"abstract":"In medical image classification, uncertainty estimation providing confidence of decision is part of interpretability of prediction model. Based on estimated uncertainty, physicians can pick out cases with high uncertainty for further inspection. However, in this uncertainty-informed decision referral, models may make wrong predictions with high certainty which leads to omission of false predictions. Therefore, we propose a method to set up a model which could make correct prediction with low uncertainty and wrong prediction with high uncertainty. We integrate uncertainty estimation into training phase and design a novel loss function “penalized entropy” by penalizing wrong but certain samples to improve the models' certainty performance. Experiments were conducted on three datasets: optical coherence tomography (OCT) image dataset for anti-vascular endothelial growth factor (anti- VEGF) effectiveness classification, OCT image dataset for diagnostic classification, and chest X-ray dataset for pneumonia classification. Performances were evaluated on both accuracy metrics such as accuracy, sensitivity, specificity, area under the curve (AVC), and certainty metrics which are accuracy vs. uncertainty (AvV), probability of correct results among certain predictions (PCC), and probability of uncertain results among wrong predictions (PUW). Results show that the method using the proposed loss function can achieve better or comparable accuracy and state-of-the-art certainty performance.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"25 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":"132527024","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

Automatic Detection of Prostate Cancer Systemic Lesions Based on Deep Learning and 68Ga-PSMA-11 PET/CT 基于深度学习和68Ga-PSMA-11 PET/CT的前列腺癌全身病变自动检测

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00065

S. Zhong, Yuxuan Wu, Zhantao Liu, Zhaohong Pan, Bingsheng Huang, Qinqin Yang

引用次数: 0

Policy-Based Diabetes Detection using Formal Runtime Verification Monitors 使用正式运行时验证监视器的基于策略的糖尿病检测

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00066

Abhinandan Panda, Srinivas Pinisetty, P. Roop

引用次数: 0

Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection 基于深度学习的面部皱纹检测半自动标记和训练策略

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00075

Semin Kim, Huisu Yoon, Jonghan Lee, S. Yoo

{"title":"Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection","authors":"Semin Kim, Huisu Yoon, Jonghan Lee, S. Yoo","doi":"10.1109/CBMS55023.2022.00075","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00075","url":null,"abstract":"Facial wrinkle is very important in measuring aging. Image processing-based methods have been proposed for wrinkle detection, but their performance was not enough because wrinkles have a wide variety of thickness, shape, orientation, and vague boundaries. Recently, deep learning-based methods have been widely applied in the field of image recognition with a lot of labeled image dataset. To extend this technology to facial wrinkle detection, labeling work for wrinkles to generate ground truth is very important. However, it is difficult to label wrinkles accurately because of the wide variety. In this paper, we propose a semiautomatic labeling strategy incorporating a texture map and a deep learning model. Specifically, the proposed method extracted the texture map from an original image and removed non-wrinkle textures on the map by multiplying with a roughly labeled wrinkle mask. Then, the map is converted into ground truth by thresholding. Using the ground truth, a deep learning model was trained with the original image and the texture map. The trained model was evaluated with facial images obtained from real skin diagnosis devices, and the results showed superior performance to those of existing image processing-based methods.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"2674 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":"122537572","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: 3

Patient identification methods based on medical imagery and their impact on patient privacy and open medical data 基于医学图像的患者识别方法及其对患者隐私和开放医疗数据的影响

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00079

Laura Carolina Martínez Esmeral, A. Uhl

引用次数: 1

Towards Evidence-based Argumentation Graph for Clinical Decision Support 面向临床决策支持的循证论证图

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00078

Liang Xiao

引用次数: 1

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps polyconnect:用于生成具有息肉的逼真胃肠道图像的图像绘制

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-05-30 DOI: 10.1109/CBMS55023.2022.00019

Jan Andre Fagereng, Vajira Lasantha Thambawita, A. Storaas, S. Parasa, T. Lange, P. Halvorsen, M. Riegler

{"title":"PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps","authors":"Jan Andre Fagereng, Vajira Lasantha Thambawita, A. Storaas, S. Parasa, T. Lange, P. Halvorsen, M. Riegler","doi":"10.1109/CBMS55023.2022.00019","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00019","url":null,"abstract":"Early identification of a polyp in the lower gas-trointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when building CAD systems. Generating synthetic medical data is an active research area to overcome the problem of having relatively few true positive cases in the medical domain. To be able to efficiently train machine learning (ML) models, which are the core of CAD systems, a considerable amount of data should be used. In this respect, we propose the PolypConnect pipeline, which can convert non-polyp images into polyp images to increase the size of training datasets for training. We present the whole pipeline with quantitative and qualitative evaluations involving endoscopists. The polyp segmentation model trained using synthetic data, and real data shows a 5.1% improvement of mean intersection over union (mIOU), compared to the model trained only using real data. The codes of all the experiments are available on GitHub to reproduce the results.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116428454","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: 3

FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction 重症监护病房死亡率预测的联邦学习工作流

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-05-30 DOI: 10.1109/CBMS55023.2022.00013

Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, P. Papapetrou

{"title":"FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction","authors":"Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollmén, P. Papapetrou","doi":"10.1109/CBMS55023.2022.00013","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00013","url":null,"abstract":"Although Machine Learning can be seen as a promising tool to improve clinical decision-making, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional Machine Learning challenging. Federated Learning can counteract those limitations by training Machine Learning models over data silos while keeping the sensitive data localized. This study proposes a Federated Learning workflow for Intensive Care Unit mortality prediction. Hereby, the applicability of Federated Learning as an alternative to Centralized Machine Learning and Local Machine Learning is investigated by introducing Federated Learning to the binary classification problem of predicting Intensive Care Unit mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, and 48h) and the number of Federated Learning clients (2, 4, and 8). The experiments demonstrate that both Centralized Machine Learning and Federated Learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over Local Machine Learning. Thus, Federated Learning can be seen as a valid and privacy-preserving alternative to Centralized Machine Learning for classifying Intensive Care Unit mortality when the sharing of sensitive patient data between hospitals is not possible.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131639099","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

Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning 使用机器学习预测肾移植患者他克莫司暴露

2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2022-05-09 DOI: 10.1109/CBMS55023.2022.00014

A. Storaas, A. Aasberg, P. Halvorsen, M. Riegler, Inga Strumke

引用次数: 0