Matches in SemOpenAlex for { <https://semopenalex.org/work/W3016888800> ?p ?o ?g. }
- W3016888800 endingPage "2489" @default.
- W3016888800 startingPage "2481" @default.
- W3016888800 abstract "Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception." @default.
- W3016888800 created "2020-04-24" @default.
- W3016888800 creator A5002716505 @default.
- W3016888800 creator A5008560050 @default.
- W3016888800 creator A5012356889 @default.
- W3016888800 creator A5016065545 @default.
- W3016888800 creator A5024638568 @default.
- W3016888800 creator A5038525205 @default.
- W3016888800 creator A5048626751 @default.
- W3016888800 creator A5086439379 @default.
- W3016888800 creator A5089132290 @default.
- W3016888800 date "2020-09-01" @default.
- W3016888800 modified "2023-10-17" @default.
- W3016888800 title "Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network" @default.
- W3016888800 cites W1896424170 @default.
- W3016888800 cites W1977064958 @default.
- W3016888800 cites W1990881910 @default.
- W3016888800 cites W2027390962 @default.
- W3016888800 cites W2044594213 @default.
- W3016888800 cites W2062996161 @default.
- W3016888800 cites W2077492780 @default.
- W3016888800 cites W2097599147 @default.
- W3016888800 cites W2126494533 @default.
- W3016888800 cites W2140577765 @default.
- W3016888800 cites W2194775991 @default.
- W3016888800 cites W2295107390 @default.
- W3016888800 cites W2560023338 @default.
- W3016888800 cites W2589089889 @default.
- W3016888800 cites W2620200771 @default.
- W3016888800 cites W2753709519 @default.
- W3016888800 cites W2765376188 @default.
- W3016888800 cites W2766922223 @default.
- W3016888800 cites W2769143887 @default.
- W3016888800 cites W2786524046 @default.
- W3016888800 cites W2798365843 @default.
- W3016888800 cites W2798553619 @default.
- W3016888800 cites W2809443510 @default.
- W3016888800 cites W2888905826 @default.
- W3016888800 cites W2890385962 @default.
- W3016888800 cites W2893214677 @default.
- W3016888800 cites W2899335103 @default.
- W3016888800 cites W2899465276 @default.
- W3016888800 cites W2901075030 @default.
- W3016888800 cites W2911834142 @default.
- W3016888800 cites W2949419500 @default.
- W3016888800 cites W2963196212 @default.
- W3016888800 cites W2963313410 @default.
- W3016888800 cites W2963377935 @default.
- W3016888800 cites W2964227007 @default.
- W3016888800 doi "https://doi.org/10.1109/jbhi.2020.2986376" @default.
- W3016888800 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32310809" @default.
- W3016888800 hasPublicationYear "2020" @default.
- W3016888800 type Work @default.
- W3016888800 sameAs 3016888800 @default.
- W3016888800 citedByCount "120" @default.
- W3016888800 countsByYear W30168888002020 @default.
- W3016888800 countsByYear W30168888002021 @default.
- W3016888800 countsByYear W30168888002022 @default.
- W3016888800 countsByYear W30168888002023 @default.
- W3016888800 crossrefType "journal-article" @default.
- W3016888800 hasAuthorship W3016888800A5002716505 @default.
- W3016888800 hasAuthorship W3016888800A5008560050 @default.
- W3016888800 hasAuthorship W3016888800A5012356889 @default.
- W3016888800 hasAuthorship W3016888800A5016065545 @default.
- W3016888800 hasAuthorship W3016888800A5024638568 @default.
- W3016888800 hasAuthorship W3016888800A5038525205 @default.
- W3016888800 hasAuthorship W3016888800A5048626751 @default.
- W3016888800 hasAuthorship W3016888800A5086439379 @default.
- W3016888800 hasAuthorship W3016888800A5089132290 @default.
- W3016888800 hasConcept C108583219 @default.
- W3016888800 hasConcept C115961682 @default.
- W3016888800 hasConcept C119857082 @default.
- W3016888800 hasConcept C124504099 @default.
- W3016888800 hasConcept C12713177 @default.
- W3016888800 hasConcept C138885662 @default.
- W3016888800 hasConcept C153180895 @default.
- W3016888800 hasConcept C154945302 @default.
- W3016888800 hasConcept C162324750 @default.
- W3016888800 hasConcept C187736073 @default.
- W3016888800 hasConcept C2779744641 @default.
- W3016888800 hasConcept C2780451532 @default.
- W3016888800 hasConcept C31972630 @default.
- W3016888800 hasConcept C41008148 @default.
- W3016888800 hasConcept C41895202 @default.
- W3016888800 hasConcept C50644808 @default.
- W3016888800 hasConcept C52622490 @default.
- W3016888800 hasConcept C75294576 @default.
- W3016888800 hasConcept C81363708 @default.
- W3016888800 hasConcept C89600930 @default.
- W3016888800 hasConcept C97931131 @default.
- W3016888800 hasConceptScore W3016888800C108583219 @default.
- W3016888800 hasConceptScore W3016888800C115961682 @default.
- W3016888800 hasConceptScore W3016888800C119857082 @default.
- W3016888800 hasConceptScore W3016888800C124504099 @default.
- W3016888800 hasConceptScore W3016888800C12713177 @default.
- W3016888800 hasConceptScore W3016888800C138885662 @default.
- W3016888800 hasConceptScore W3016888800C153180895 @default.
- W3016888800 hasConceptScore W3016888800C154945302 @default.