Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation最新文献

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AUTOMATED DETECTION OF MALARIAL RETINOPATHY USING TRANSFER LEARNING. 利用迁移学习自动检测疟原虫视网膜病变。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2020-03-01 Epub Date: 2020-05-18 DOI: 10.1109/ssiai49293.2020.9094595

A Kurup, P Soliz, S Nemeth, V Joshi

{"title":"AUTOMATED DETECTION OF MALARIAL RETINOPATHY USING TRANSFER LEARNING.","authors":"A Kurup, P Soliz, S Nemeth, V Joshi","doi":"10.1109/ssiai49293.2020.9094595","DOIUrl":"10.1109/ssiai49293.2020.9094595","url":null,"abstract":"Cerebral Malaria (CM) is a severe neurological syndrome of malaria mainly found in children and is associated with highly specific retinal lesions. The manifestation of these indications of CM in the retina is called malarial retinopathy (MR). All patients showing clinical signs of CM are commonly diagnosed and treated accordingly; however, 23% of them are misdiagnosed as they suffer from another infection with identical clinical symptoms. Due to these underlying symptoms, the false positive cases may go untreated and could result in death of the patients. A diagnostic test is needed that is highly specific in order to reduce false positives. The purpose of this study to demonstrate a technique based on a transfer learning technique using images from three different retinal cameras to identify the hemorrhages and whitening lesions in the retina which can accurately identify the patients with MR. The MR detection model gives a specificity of 100% and a sensitivity of 90% with an AUC of 0.98. The algorithm demonstrates the potential of accurate MR detection with a low-cost retinal camera.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2020 ","pages":"18-21"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591150/pdf/nihms-1636320.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38541238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Maximum-Likelihood Approach for ADC Estimation of Lesions in Visceral Organs. 内脏器官病变 ADC 估算的最大似然法

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2012-01-01 DOI: 10.1109/SSIAI.2012.6202443

Abhinav K Jha, Jeffrey J Rodríguez

{"title":"A Maximum-Likelihood Approach for ADC Estimation of Lesions in Visceral Organs.","authors":"Abhinav K Jha, Jeffrey J Rodríguez","doi":"10.1109/SSIAI.2012.6202443","DOIUrl":"10.1109/SSIAI.2012.6202443","url":null,"abstract":"Accurate estimation of the apparent diffusion coefficient (ADC) of lesions in diffusion-weighted magnetic resonance imaging (DWMRI) is important to predict and monitor anti-cancer therapy response. The task of ADC estimation of lesions is complicated due to noise in the image, different variances in signal strengths at different b values and other random phenomena. In organs that have visceral motion, due to motion across scans, estimating the ADC becomes even more complex. To get rid of inaccuracies due to motion, only a single ADC value of the lesion is estimated, conventionally using a linear-regression (LR) approach. The LR approach is based on an inaccurate noise model and also suffers from other deficiencies. In this paper, we propose an easy-to-implement and computationally-fast maximum-likelihood (ML) method to estimate the ADC value of heterogeneous lesions in visceral organs. The proposed method takes into account the Rician distribution of noise in DWMRI. In the process, we also derive the statistical model for the measured mean signal intensity in DWMRI. We show using Monte-Carlo simulations that that the proposed method is more accurate than the LR method.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2012 ","pages":"21-24"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4166577/pdf/nihms394556.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32685578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images. 弥散加权MR图像肝脏病灶分割的聚类算法。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2010-05-23 DOI: 10.1109/SSIAI.2010.5483911

Abhinav K Jha, Jeffrey J Rodríguez, Renu M Stephen, Alison T Stopeck

引用次数: 25