FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288049169> ?p ?o ?g. }

• W4288049169 endingPage "2317" @default.
• W4288049169 startingPage "2304" @default.
• W4288049169 abstract "Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: cumulative relation, relative relation, maximal relation and minimal relation. These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side)." @default.
• W4288049169 created "2022-07-27" @default.
• W4288049169 creator A5049789399 @default.
• W4288049169 creator A5058723139 @default.
• W4288049169 creator A5064975660 @default.
• W4288049169 creator A5069652684 @default.
• W4288049169 date "2022-09-01" @default.
• W4288049169 modified "2023-10-14" @default.
• W4288049169 title "Deep Relation Learning for Regression and Its Application to Brain Age Estimation" @default.
• W4288049169 cites W1109087368 @default.
• W4288049169 cites W1458229720 @default.
• W4288049169 cites W1980114101 @default.
• W4288049169 cites W1998523455 @default.
• W4288049169 cites W2000539875 @default.
• W4288049169 cites W2004691878 @default.
• W4288049169 cites W2079987516 @default.
• W4288049169 cites W2097117768 @default.
• W4288049169 cites W2106235379 @default.
• W4288049169 cites W2117340355 @default.
• W4288049169 cites W2155188667 @default.
• W4288049169 cites W2167393047 @default.
• W4288049169 cites W2167868121 @default.
• W4288049169 cites W2194775991 @default.
• W4288049169 cites W2294130536 @default.
• W4288049169 cites W2579617530 @default.
• W4288049169 cites W2592232824 @default.
• W4288049169 cites W2784886794 @default.
• W4288049169 cites W2810442486 @default.
• W4288049169 cites W2919115771 @default.
• W4288049169 cites W2929665219 @default.
• W4288049169 cites W2951152770 @default.
• W4288049169 cites W2960206397 @default.
• W4288049169 cites W2961560364 @default.
• W4288049169 cites W2962989546 @default.
• W4288049169 cites W2963446712 @default.
• W4288049169 cites W2963854351 @default.
• W4288049169 cites W2964350391 @default.
• W4288049169 cites W2976981404 @default.
• W4288049169 cites W2981165461 @default.
• W4288049169 cites W2984423913 @default.
• W4288049169 cites W2990366895 @default.
• W4288049169 cites W2993515580 @default.
• W4288049169 cites W2995865170 @default.
• W4288049169 cites W3007486523 @default.
• W4288049169 cites W3007648700 @default.
• W4288049169 cites W3010138595 @default.
• W4288049169 cites W3024838586 @default.
• W4288049169 cites W3028572934 @default.
• W4288049169 cites W3033794115 @default.
• W4288049169 cites W3037286432 @default.
• W4288049169 cites W3041258432 @default.
• W4288049169 cites W3043840704 @default.
• W4288049169 cites W3046299245 @default.
• W4288049169 cites W3092783643 @default.
• W4288049169 cites W3092858474 @default.
• W4288049169 cites W3121045849 @default.
• W4288049169 cites W3137989279 @default.
• W4288049169 cites W3141637546 @default.
• W4288049169 cites W3157644970 @default.
• W4288049169 cites W3161192540 @default.
• W4288049169 cites W3172863135 @default.
• W4288049169 cites W3176196997 @default.
• W4288049169 cites W3185157240 @default.
• W4288049169 cites W3191900348 @default.
• W4288049169 cites W3192252093 @default.
• W4288049169 cites W4225336162 @default.
• W4288049169 cites W4226185080 @default.
• W4288049169 doi "https://doi.org/10.1109/tmi.2022.3161739" @default.
• W4288049169 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35320092" @default.
• W4288049169 hasPublicationYear "2022" @default.
• W4288049169 type Work @default.
• W4288049169 citedByCount "6" @default.
• W4288049169 countsByYear W42880491692023 @default.
• W4288049169 crossrefType "journal-article" @default.
• W4288049169 hasAuthorship W4288049169A5049789399 @default.
• W4288049169 hasAuthorship W4288049169A5058723139 @default.
• W4288049169 hasAuthorship W4288049169A5064975660 @default.
• W4288049169 hasAuthorship W4288049169A5069652684 @default.
• W4288049169 hasBestOaLocation W42880491692 @default.
• W4288049169 hasConcept C105795698 @default.
• W4288049169 hasConcept C108583219 @default.
• W4288049169 hasConcept C119857082 @default.
• W4288049169 hasConcept C124101348 @default.
• W4288049169 hasConcept C138885662 @default.
• W4288049169 hasConcept C152877465 @default.
• W4288049169 hasConcept C153180895 @default.
• W4288049169 hasConcept C153604712 @default.
• W4288049169 hasConcept C154945302 @default.
• W4288049169 hasConcept C25343380 @default.
• W4288049169 hasConcept C2776401178 @default.
• W4288049169 hasConcept C33923547 @default.
• W4288049169 hasConcept C41008148 @default.
• W4288049169 hasConcept C41895202 @default.
• W4288049169 hasConcept C48921125 @default.
• W4288049169 hasConcept C50644808 @default.
• W4288049169 hasConcept C52622490 @default.
• W4288049169 hasConcept C81363708 @default.
• W4288049169 hasConcept C83546350 @default.

• next