FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224986648> ?p ?o ?g. }

• W4224986648 abstract "Alzheimer's disease (AD) is an age-related disease that affects a large proportion of the elderly. Currently, the neuroimaging techniques [e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)] are promising modalities for AD diagnosis. Since not all brain regions are affected by AD, a common technique is to study some region-of-interests (ROIs) that are believed to be closely related to AD. Conventional methods used ROIs, identified by the handcrafted features through Automated Anatomical Labeling (AAL) atlas rather than utilizing the original images which may induce missing informative features. In addition, they learned their framework based on the discriminative patches instead of full images for AD diagnosis in multistage learning scheme. In this paper, we integrate the original image features from MRI and PET with their ROIs features in one learning process. Furthermore, we use the ROIs features for forcing the network to focus on the regions that is highly related to AD and hence, the performance of the AD diagnosis can be improved. Specifically, we first obtain the ROIs features from the AAL, then we register every ROI with its corresponding region of the original image to get a synthetic image for each modality of every subject. Then, we employ the convolutional auto-encoder network for learning the synthetic image features and the convolutional neural network (CNN) for learning the original image features. Meanwhile, we concatenate the features from both networks after each convolution layer. Finally, the highly learned features from the MRI and PET are concatenated for brain disease classification. Experiments are carried out on the ADNI datasets including ADNI-1 and ADNI-2 to evaluate our method performance. Our method demonstrates a higher performance in brain disease classification than the recent studies." @default.
• W4224986648 created "2022-04-28" @default.
• W4224986648 creator A5034417173 @default.
• W4224986648 creator A5042305251 @default.
• W4224986648 creator A5046543349 @default.
• W4224986648 date "2022-04-28" @default.
• W4224986648 modified "2023-09-30" @default.
• W4224986648 title "Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer’s Disease Diagnosis" @default.
• W4224986648 cites W1521781547 @default.
• W4224986648 cites W1892035143 @default.
• W4224986648 cites W1992117549 @default.
• W4224986648 cites W2000292092 @default.
• W4224986648 cites W2054540100 @default.
• W4224986648 cites W2058046532 @default.
• W4224986648 cites W2085885728 @default.
• W4224986648 cites W2126598020 @default.
• W4224986648 cites W2136573752 @default.
• W4224986648 cites W2146089088 @default.
• W4224986648 cites W2147800946 @default.
• W4224986648 cites W2169366712 @default.
• W4224986648 cites W2171405125 @default.
• W4224986648 cites W2179914359 @default.
• W4224986648 cites W2345678177 @default.
• W4224986648 cites W2527896982 @default.
• W4224986648 cites W2577040328 @default.
• W4224986648 cites W2737584517 @default.
• W4224986648 cites W2743129437 @default.
• W4224986648 cites W2765741717 @default.
• W4224986648 cites W2783188875 @default.
• W4224986648 cites W2788043421 @default.
• W4224986648 cites W2791282053 @default.
• W4224986648 cites W2899335103 @default.
• W4224986648 cites W2905035821 @default.
• W4224986648 cites W2942489367 @default.
• W4224986648 cites W2942882625 @default.
• W4224986648 cites W2944541202 @default.
• W4224986648 cites W2957099944 @default.
• W4224986648 cites W2991909137 @default.
• W4224986648 cites W3097518530 @default.
• W4224986648 cites W3112381424 @default.
• W4224986648 cites W3118857593 @default.
• W4224986648 cites W3120990036 @default.
• W4224986648 cites W3127167602 @default.
• W4224986648 cites W3154068005 @default.
• W4224986648 cites W3172546465 @default.
• W4224986648 cites W3178350790 @default.
• W4224986648 doi "https://doi.org/10.3389/fnagi.2022.812870" @default.
• W4224986648 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35572142" @default.
• W4224986648 hasPublicationYear "2022" @default.
• W4224986648 type Work @default.
• W4224986648 citedByCount "0" @default.
• W4224986648 crossrefType "journal-article" @default.
• W4224986648 hasAuthorship W4224986648A5034417173 @default.
• W4224986648 hasAuthorship W4224986648A5042305251 @default.
• W4224986648 hasAuthorship W4224986648A5046543349 @default.
• W4224986648 hasBestOaLocation W42249866481 @default.
• W4224986648 hasConcept C108583219 @default.
• W4224986648 hasConcept C126838900 @default.
• W4224986648 hasConcept C153180895 @default.
• W4224986648 hasConcept C154945302 @default.
• W4224986648 hasConcept C15744967 @default.
• W4224986648 hasConcept C169760540 @default.
• W4224986648 hasConcept C2775842073 @default.
• W4224986648 hasConcept C31972630 @default.
• W4224986648 hasConcept C41008148 @default.
• W4224986648 hasConcept C58693492 @default.
• W4224986648 hasConcept C71924100 @default.
• W4224986648 hasConcept C81363708 @default.
• W4224986648 hasConcept C97931131 @default.
• W4224986648 hasConceptScore W4224986648C108583219 @default.
• W4224986648 hasConceptScore W4224986648C126838900 @default.
• W4224986648 hasConceptScore W4224986648C153180895 @default.
• W4224986648 hasConceptScore W4224986648C154945302 @default.
• W4224986648 hasConceptScore W4224986648C15744967 @default.
• W4224986648 hasConceptScore W4224986648C169760540 @default.
• W4224986648 hasConceptScore W4224986648C2775842073 @default.
• W4224986648 hasConceptScore W4224986648C31972630 @default.
• W4224986648 hasConceptScore W4224986648C41008148 @default.
• W4224986648 hasConceptScore W4224986648C58693492 @default.
• W4224986648 hasConceptScore W4224986648C71924100 @default.
• W4224986648 hasConceptScore W4224986648C81363708 @default.
• W4224986648 hasConceptScore W4224986648C97931131 @default.
• W4224986648 hasLocation W42249866481 @default.
• W4224986648 hasLocation W42249866482 @default.
• W4224986648 hasLocation W42249866483 @default.
• W4224986648 hasOpenAccess W4224986648 @default.
• W4224986648 hasPrimaryLocation W42249866481 @default.
• W4224986648 hasRelatedWork W2024160000 @default.
• W4224986648 hasRelatedWork W2729514902 @default.
• W4224986648 hasRelatedWork W2731899572 @default.
• W4224986648 hasRelatedWork W2999805992 @default.
• W4224986648 hasRelatedWork W3116150086 @default.
• W4224986648 hasRelatedWork W3133861977 @default.
• W4224986648 hasRelatedWork W4200173597 @default.
• W4224986648 hasRelatedWork W4312417841 @default.
• W4224986648 hasRelatedWork W4321369474 @default.
• W4224986648 hasRelatedWork W2073139667 @default.
• W4224986648 hasVolume "14" @default.
• W4224986648 isParatext "false" @default.
• W4224986648 isRetracted "false" @default.

• next