FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2943412658> ?p ?o ?g. }

• W2943412658 endingPage "604" @default.
• W2943412658 startingPage "597" @default.
• W2943412658 abstract "Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However, a key barrier in the required training of CNNs is obtaining large-scale and precisely annotated imaging data. We sought to address the lack of annotated data with eye tracking technology. As a proof of principle, our hypothesis was that segmentation masks generated with the help of eye tracking (ET) would be very similar to those rendered by hand annotation (HA). Additionally, our goal was to show that a CNN trained on ET masks would be equivalent to one trained on HA masks, the latter being the current standard approach. Step 1: Screen captures of 19 publicly available radiologic images of assorted structures within various modalities were analyzed. ET and HA masks for all regions of interest (ROIs) were generated from these image datasets. Step 2: Utilizing a similar approach, ET and HA masks for 356 publicly available T1-weighted postcontrast meningioma images were generated. Three hundred six of these image + mask pairs were used to train a CNN with U-net-based architecture. The remaining 50 images were used as the independent test set. Step 1: ET and HA masks for the nonneurological images had an average Dice similarity coefficient (DSC) of 0.86 between each other. Step 2: Meningioma ET and HA masks had an average DSC of 0.85 between each other. After separate training using both approaches, the ET approach performed virtually identically to HA on the test set of 50 images. The former had an area under the curve (AUC) of 0.88, while the latter had AUC of 0.87. ET and HA predictions had trimmed mean DSCs compared to the original HA maps of 0.73 and 0.74, respectively. These trimmed DSCs between ET and HA were found to be statistically equivalent with a p value of 0.015. We have demonstrated that ET can create segmentation masks suitable for deep learning semantic segmentation. Future work will integrate ET to produce masks in a faster, more natural manner that distracts less from typical radiology clinical workflow." @default.
• W2943412658 created "2019-05-09" @default.
• W2943412658 creator A5007454880 @default.
• W2943412658 creator A5009622902 @default.
• W2943412658 creator A5012995283 @default.
• W2943412658 creator A5030188696 @default.
• W2943412658 creator A5049843981 @default.
• W2943412658 creator A5060896454 @default.
• W2943412658 creator A5068012456 @default.
• W2943412658 creator A5074397580 @default.
• W2943412658 creator A5074886468 @default.
• W2943412658 creator A5086345223 @default.
• W2943412658 creator A5087061886 @default.
• W2943412658 date "2019-05-01" @default.
• W2943412658 modified "2023-10-16" @default.
• W2943412658 title "Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks" @default.
• W2943412658 cites W1990418160 @default.
• W2943412658 cites W1998613859 @default.
• W2943412658 cites W2019997477 @default.
• W2943412658 cites W2020388915 @default.
• W2943412658 cites W2025033911 @default.
• W2943412658 cites W2029440658 @default.
• W2943412658 cites W2037698344 @default.
• W2943412658 cites W2069855563 @default.
• W2943412658 cites W2131378922 @default.
• W2943412658 cites W2150671093 @default.
• W2943412658 cites W2513717809 @default.
• W2943412658 cites W2560280736 @default.
• W2943412658 cites W2561022822 @default.
• W2943412658 cites W2592929672 @default.
• W2943412658 cites W2610765860 @default.
• W2943412658 cites W2616065668 @default.
• W2943412658 cites W2725984455 @default.
• W2943412658 cites W2729876886 @default.
• W2943412658 cites W2731899572 @default.
• W2943412658 cites W2784963350 @default.
• W2943412658 cites W2789992731 @default.
• W2943412658 cites W2790011672 @default.
• W2943412658 cites W2790984391 @default.
• W2943412658 cites W2792185347 @default.
• W2943412658 cites W2804063501 @default.
• W2943412658 cites W2886717759 @default.
• W2943412658 cites W2898197178 @default.
• W2943412658 cites W2903291548 @default.
• W2943412658 cites W2962884052 @default.
• W2943412658 cites W4320801073 @default.
• W2943412658 doi "https://doi.org/10.1007/s10278-019-00220-4" @default.
• W2943412658 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6646645" @default.
• W2943412658 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31044392" @default.
• W2943412658 hasPublicationYear "2019" @default.
• W2943412658 type Work @default.
• W2943412658 sameAs 2943412658 @default.
• W2943412658 citedByCount "32" @default.
• W2943412658 countsByYear W29434126582020 @default.
• W2943412658 countsByYear W29434126582021 @default.
• W2943412658 countsByYear W29434126582022 @default.
• W2943412658 countsByYear W29434126582023 @default.
• W2943412658 crossrefType "journal-article" @default.
• W2943412658 hasAuthorship W2943412658A5007454880 @default.
• W2943412658 hasAuthorship W2943412658A5009622902 @default.
• W2943412658 hasAuthorship W2943412658A5012995283 @default.
• W2943412658 hasAuthorship W2943412658A5030188696 @default.
• W2943412658 hasAuthorship W2943412658A5049843981 @default.
• W2943412658 hasAuthorship W2943412658A5060896454 @default.
• W2943412658 hasAuthorship W2943412658A5068012456 @default.
• W2943412658 hasAuthorship W2943412658A5074397580 @default.
• W2943412658 hasAuthorship W2943412658A5074886468 @default.
• W2943412658 hasAuthorship W2943412658A5086345223 @default.
• W2943412658 hasAuthorship W2943412658A5087061886 @default.
• W2943412658 hasBestOaLocation W29434126581 @default.
• W2943412658 hasConcept C103278499 @default.
• W2943412658 hasConcept C108583219 @default.
• W2943412658 hasConcept C115961682 @default.
• W2943412658 hasConcept C124504099 @default.
• W2943412658 hasConcept C153180895 @default.
• W2943412658 hasConcept C154945302 @default.
• W2943412658 hasConcept C163892561 @default.
• W2943412658 hasConcept C169903167 @default.
• W2943412658 hasConcept C31601959 @default.
• W2943412658 hasConcept C31972630 @default.
• W2943412658 hasConcept C41008148 @default.
• W2943412658 hasConcept C81363708 @default.
• W2943412658 hasConcept C89600930 @default.
• W2943412658 hasConceptScore W2943412658C103278499 @default.
• W2943412658 hasConceptScore W2943412658C108583219 @default.
• W2943412658 hasConceptScore W2943412658C115961682 @default.
• W2943412658 hasConceptScore W2943412658C124504099 @default.
• W2943412658 hasConceptScore W2943412658C153180895 @default.
• W2943412658 hasConceptScore W2943412658C154945302 @default.
• W2943412658 hasConceptScore W2943412658C163892561 @default.
• W2943412658 hasConceptScore W2943412658C169903167 @default.
• W2943412658 hasConceptScore W2943412658C31601959 @default.
• W2943412658 hasConceptScore W2943412658C31972630 @default.
• W2943412658 hasConceptScore W2943412658C41008148 @default.
• W2943412658 hasConceptScore W2943412658C81363708 @default.
• W2943412658 hasConceptScore W2943412658C89600930 @default.
• W2943412658 hasIssue "4" @default.
• W2943412658 hasLocation W29434126581 @default.

• next