Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. MILLanD (Workshop) (1st : 2022 : Singapore)最新文献

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Image Quality Classification for Automated Visual Evaluation of Cervical Precancer. 用于宫颈癌前病变自动视觉评估的图像质量分类。
Zhiyun Xue, Sandeep Angara, Peng Guo, Sivaramakrishnan Rajaraman, Jose Jeronimo, Ana Cecilia Rodriguez, Karla Alfaro, Kittipat Charoenkwan, Chemtai Mungo, Joel Fokom Domgue, Nicolas Wentzensen, Kanan T Desai, Kayode Olusegun Ajenifuja, Elisabeth Wikström, Brian Befano, Silvia de Sanjosé, Mark Schiffman, Sameer Antani
{"title":"Image Quality Classification for Automated Visual Evaluation of Cervical Precancer.","authors":"Zhiyun Xue,&nbsp;Sandeep Angara,&nbsp;Peng Guo,&nbsp;Sivaramakrishnan Rajaraman,&nbsp;Jose Jeronimo,&nbsp;Ana Cecilia Rodriguez,&nbsp;Karla Alfaro,&nbsp;Kittipat Charoenkwan,&nbsp;Chemtai Mungo,&nbsp;Joel Fokom Domgue,&nbsp;Nicolas Wentzensen,&nbsp;Kanan T Desai,&nbsp;Kayode Olusegun Ajenifuja,&nbsp;Elisabeth Wikström,&nbsp;Brian Befano,&nbsp;Silvia de Sanjosé,&nbsp;Mark Schiffman,&nbsp;Sameer Antani","doi":"10.1007/978-3-031-16760-7_20","DOIUrl":"10.1007/978-3-031-16760-7_20","url":null,"abstract":"<p><p>Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories (\"unusable\", \"unsatisfactory\", \"limited\", and \"evaluable\") and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.</p>","PeriodicalId":74146,"journal":{"name":"Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. MILLanD (Workshop) (1st : 2022 : Singapore)","volume":"13559 ","pages":"206-217"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614805/pdf/nihms-1840611.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10821372","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
Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 有限和噪声数据的医学图像学习:第一届国际研讨会,MILLanD 2022,与MICCAI 2022一起举行,新加坡,2022年9月22日,会议录
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引用次数: 1
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