Matches in SemOpenAlex for { <https://semopenalex.org/work/W2979586540> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W2979586540 endingPage "252" @default.
- W2979586540 startingPage "243" @default.
- W2979586540 abstract "Intracranial aneurysm rupture can cause a serious stroke, which is related to the decline of daily life ability of the elderly. Although deep learning is now the most successful solution for organ detection, it requires myriads of training data, consistent of the image format, and a balanced sample distribution. This work presents an innovative representation of intracranial aneurysm detection as a shape analysis problem rather than a computer vision problem. We detected intracranial aneurysms in 3D cerebrovascular mesh models after segmentation of the brain vessels from the medical images, which can overcome the barriers of data format and data distribution, serving both clinical and screening purposes. Additionally, we propose a transferable multi-model ensemble (MMEN) architecture to detect intracranial aneurysms from cerebrovascular mesh models with limited data. To obtain a well-defined convolution operator, we use a global seamless parameterization converting a 3D cerebrovascular mesh model to a planar flat-torus. In the architecture, we transfer the planar flat-torus presentation abilities of three GoogleNet Inception V3 models, which were pre-trained on the ImageNet database, to characterize the intracranial aneurysms with local and global geometric features such as Gaussian curvature (GC), shape diameter function (SDF) and wave kernel signature (WKS), respectively. We jointly utilize all three models to detect aneurysms with adaptive weights learning based on back propagation. The experimental results on the 121 models show that our proposed method can achieve detection accuracy of 95.1% with 94.7% F1-score and 94.8% sensitivity, which is as good as the state-of-art work but is applicable to inhomogeneous image modalities and smaller datasets." @default.
- W2979586540 created "2019-10-18" @default.
- W2979586540 creator A5012772312 @default.
- W2979586540 creator A5024691662 @default.
- W2979586540 creator A5049192404 @default.
- W2979586540 creator A5066033124 @default.
- W2979586540 date "2019-01-01" @default.
- W2979586540 modified "2023-09-25" @default.
- W2979586540 title "Intracranial Aneurysm Detection from 3D Vascular Mesh Models with Ensemble Deep Learning" @default.
- W2979586540 cites W2094366023 @default.
- W2979586540 cites W2095738590 @default.
- W2979586540 cites W2097117768 @default.
- W2979586540 cites W2194775991 @default.
- W2979586540 cites W2288040786 @default.
- W2979586540 cites W2674344579 @default.
- W2979586540 cites W2737081152 @default.
- W2979586540 cites W2746587344 @default.
- W2979586540 cites W2895926594 @default.
- W2979586540 cites W2904807675 @default.
- W2979586540 cites W2915142973 @default.
- W2979586540 cites W2921137552 @default.
- W2979586540 cites W3009720000 @default.
- W2979586540 doi "https://doi.org/10.1007/978-3-030-32251-9_27" @default.
- W2979586540 hasPublicationYear "2019" @default.
- W2979586540 type Work @default.
- W2979586540 sameAs 2979586540 @default.
- W2979586540 citedByCount "12" @default.
- W2979586540 countsByYear W29795865402020 @default.
- W2979586540 countsByYear W29795865402021 @default.
- W2979586540 countsByYear W29795865402022 @default.
- W2979586540 countsByYear W29795865402023 @default.
- W2979586540 crossrefType "book-chapter" @default.
- W2979586540 hasAuthorship W2979586540A5012772312 @default.
- W2979586540 hasAuthorship W2979586540A5024691662 @default.
- W2979586540 hasAuthorship W2979586540A5049192404 @default.
- W2979586540 hasAuthorship W2979586540A5066033124 @default.
- W2979586540 hasBestOaLocation W29795865402 @default.
- W2979586540 hasConcept C108583219 @default.
- W2979586540 hasConcept C114614502 @default.
- W2979586540 hasConcept C126838900 @default.
- W2979586540 hasConcept C153180895 @default.
- W2979586540 hasConcept C154945302 @default.
- W2979586540 hasConcept C2776098176 @default.
- W2979586540 hasConcept C31972630 @default.
- W2979586540 hasConcept C33923547 @default.
- W2979586540 hasConcept C41008148 @default.
- W2979586540 hasConcept C71924100 @default.
- W2979586540 hasConcept C74193536 @default.
- W2979586540 hasConceptScore W2979586540C108583219 @default.
- W2979586540 hasConceptScore W2979586540C114614502 @default.
- W2979586540 hasConceptScore W2979586540C126838900 @default.
- W2979586540 hasConceptScore W2979586540C153180895 @default.
- W2979586540 hasConceptScore W2979586540C154945302 @default.
- W2979586540 hasConceptScore W2979586540C2776098176 @default.
- W2979586540 hasConceptScore W2979586540C31972630 @default.
- W2979586540 hasConceptScore W2979586540C33923547 @default.
- W2979586540 hasConceptScore W2979586540C41008148 @default.
- W2979586540 hasConceptScore W2979586540C71924100 @default.
- W2979586540 hasConceptScore W2979586540C74193536 @default.
- W2979586540 hasLocation W29795865401 @default.
- W2979586540 hasLocation W29795865402 @default.
- W2979586540 hasOpenAccess W2979586540 @default.
- W2979586540 hasPrimaryLocation W29795865401 @default.
- W2979586540 hasRelatedWork W1891287906 @default.
- W2979586540 hasRelatedWork W1969923398 @default.
- W2979586540 hasRelatedWork W2036807459 @default.
- W2979586540 hasRelatedWork W2060018053 @default.
- W2979586540 hasRelatedWork W2110459882 @default.
- W2979586540 hasRelatedWork W2731899572 @default.
- W2979586540 hasRelatedWork W2738221750 @default.
- W2979586540 hasRelatedWork W2772917594 @default.
- W2979586540 hasRelatedWork W2775347418 @default.
- W2979586540 hasRelatedWork W3215138031 @default.
- W2979586540 isParatext "false" @default.
- W2979586540 isRetracted "false" @default.
- W2979586540 magId "2979586540" @default.
- W2979586540 workType "book-chapter" @default.