FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210579640> ?p ?o ?g. }

• W4210579640 endingPage "1723" @default.
• W4210579640 startingPage "1711" @default.
• W4210579640 abstract "Chest X-ray is an important imaging method for the diagnosis of chest diseases. Chest radiograph diagnostic quality assessment is vital for the diagnosis of the disease because unqualified radiographs have negative impacts on doctors’ diagnosis and thus increase the burden on patients due to the re-acquirement of the radiographs. So far no algorithms and public data sets have been developed for chest radiograph diagnostic quality assessment. Towards effective chest X-ray diagnostic quality assessment, we analyze the image characteristics of four main chest radiograph diagnostic quality issues, i.e. Scapula Overlapping Lung, Artifact, Lung Field Loss, and Clavicle Unflatness. Our experiments show that general image classification methods are not competent for the task because the detailed information used for quality assessment by radiologists cannot be fully exploited by deep CNNs and image-level annotations. Then we propose to leverage a multi-label semantic segmentation framework to find the problematic regions, and then classify the quality issues based on the results of segmentation. However, subsequent classification is often negatively affected by certain small segmentation errors. Therefore, we propose to estimate a distance map that measures the distance from a pixel to its nearest segment, and use it to force the prediction of semantic segmentation more holistic and suitable for classification. Extensive experiments validate the effectiveness of our semantic-segmentation-based solution for chest X-ray diagnostic quality assessment. However, general segmentation-based algorithms requires fine pixel-wise annotations in the era of deep learning. In order to reduce reliance on fine annotations and further validate how important pixel-wise annotations are, weak supervision for segmentation is applied, and demonstrates its ability close to that of full supervision. Finally, we present ChestX-rayQuality, a chest radiograph data set, which comprises 480 frontal-view chest radiographs with semantic segmentation annotations and four labels of quality issue. Also, other 1212 chest radiographs with limited annotations are imported to validate our algorithms and arguments on larger data set. These two data set will be made publicly available." @default.
• W4210579640 created "2022-02-08" @default.
• W4210579640 creator A5016170788 @default.
• W4210579640 creator A5034339267 @default.
• W4210579640 creator A5048745902 @default.
• W4210579640 creator A5055349053 @default.
• W4210579640 date "2022-07-01" @default.
• W4210579640 modified "2023-10-09" @default.
• W4210579640 title "Chest X-Ray Diagnostic Quality Assessment: How Much Is Pixel-Wise Supervision Needed?" @default.
• W4210579640 cites W1495267108 @default.
• W4210579640 cites W2024798729 @default.
• W4210579640 cites W2030928387 @default.
• W4210579640 cites W2059862641 @default.
• W4210579640 cites W2065742895 @default.
• W4210579640 cites W2097117768 @default.
• W4210579640 cites W2117539524 @default.
• W4210579640 cites W2124351162 @default.
• W4210579640 cites W2142514727 @default.
• W4210579640 cites W2168804568 @default.
• W4210579640 cites W2194775991 @default.
• W4210579640 cites W2295107390 @default.
• W4210579640 cites W2412782625 @default.
• W4210579640 cites W2520861906 @default.
• W4210579640 cites W2552414813 @default.
• W4210579640 cites W2700195045 @default.
• W4210579640 cites W2754887662 @default.
• W4210579640 cites W2794103425 @default.
• W4210579640 cites W2885112059 @default.
• W4210579640 cites W2885373832 @default.
• W4210579640 cites W2886327376 @default.
• W4210579640 cites W2889246432 @default.
• W4210579640 cites W2953809838 @default.
• W4210579640 cites W2956648669 @default.
• W4210579640 cites W2962708065 @default.
• W4210579640 cites W2963150697 @default.
• W4210579640 cites W2963446712 @default.
• W4210579640 cites W2963466845 @default.
• W4210579640 cites W2963660453 @default.
• W4210579640 cites W2979455736 @default.
• W4210579640 cites W2979929139 @default.
• W4210579640 cites W2980311233 @default.
• W4210579640 cites W2981689412 @default.
• W4210579640 cites W3018407595 @default.
• W4210579640 cites W3027779789 @default.
• W4210579640 cites W3082755608 @default.
• W4210579640 cites W3101156210 @default.
• W4210579640 cites W3106546328 @default.
• W4210579640 doi "https://doi.org/10.1109/tmi.2022.3149171" @default.
• W4210579640 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35120002" @default.
• W4210579640 hasPublicationYear "2022" @default.
• W4210579640 type Work @default.
• W4210579640 citedByCount "4" @default.
• W4210579640 countsByYear W42105796402023 @default.
• W4210579640 crossrefType "journal-article" @default.
• W4210579640 hasAuthorship W4210579640A5016170788 @default.
• W4210579640 hasAuthorship W4210579640A5034339267 @default.
• W4210579640 hasAuthorship W4210579640A5048745902 @default.
• W4210579640 hasAuthorship W4210579640A5055349053 @default.
• W4210579640 hasConcept C106436119 @default.
• W4210579640 hasConcept C115961682 @default.
• W4210579640 hasConcept C124504099 @default.
• W4210579640 hasConcept C126838900 @default.
• W4210579640 hasConcept C142724271 @default.
• W4210579640 hasConcept C153083717 @default.
• W4210579640 hasConcept C153180895 @default.
• W4210579640 hasConcept C154945302 @default.
• W4210579640 hasConcept C160633673 @default.
• W4210579640 hasConcept C2778618615 @default.
• W4210579640 hasConcept C2781137159 @default.
• W4210579640 hasConcept C31972630 @default.
• W4210579640 hasConcept C36454342 @default.
• W4210579640 hasConcept C41008148 @default.
• W4210579640 hasConcept C55020928 @default.
• W4210579640 hasConcept C71924100 @default.
• W4210579640 hasConcept C89600930 @default.
• W4210579640 hasConceptScore W4210579640C106436119 @default.
• W4210579640 hasConceptScore W4210579640C115961682 @default.
• W4210579640 hasConceptScore W4210579640C124504099 @default.
• W4210579640 hasConceptScore W4210579640C126838900 @default.
• W4210579640 hasConceptScore W4210579640C142724271 @default.
• W4210579640 hasConceptScore W4210579640C153083717 @default.
• W4210579640 hasConceptScore W4210579640C153180895 @default.
• W4210579640 hasConceptScore W4210579640C154945302 @default.
• W4210579640 hasConceptScore W4210579640C160633673 @default.
• W4210579640 hasConceptScore W4210579640C2778618615 @default.
• W4210579640 hasConceptScore W4210579640C2781137159 @default.
• W4210579640 hasConceptScore W4210579640C31972630 @default.
• W4210579640 hasConceptScore W4210579640C36454342 @default.
• W4210579640 hasConceptScore W4210579640C41008148 @default.
• W4210579640 hasConceptScore W4210579640C55020928 @default.
• W4210579640 hasConceptScore W4210579640C71924100 @default.
• W4210579640 hasConceptScore W4210579640C89600930 @default.
• W4210579640 hasFunder F4320321001 @default.
• W4210579640 hasFunder F4320321659 @default.
• W4210579640 hasFunder F4320321885 @default.
• W4210579640 hasFunder F4320335777 @default.
• W4210579640 hasIssue "7" @default.
• W4210579640 hasLocation W42105796401 @default.

• next