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▼aAI for Brain Lesion Detection and Trauma Video Action Recognition▼h[electronic resource] :▼bFirst BONBID-HIE Lesion Segmentation Challenge and First Trauma Thompson Challenge, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 16 and 12, 2023, Proceedings /▼cedited by Rina Bao, Ellen Grant, Andrew Kirkpatrick, Juan Wachs, Yangming Ou. |
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▼a1st ed. 2025. |
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▼aCham :▼bSpringer Nature Switzerland :▼bImprint: Springer,▼c2025. |
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▼aXIV, 95 p. 29 illus., 27 illus. in color.▼bonline resource. |
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▼atext▼btxt▼2rdacontent |
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▼acomputer▼bc▼2rdamedia |
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▼atext file▼bPDF▼2rda |
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▼aLecture Notes in Computer Science,▼x1611-3349 ;▼v14567 |
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▼aBONBID-HIE 2023 -- Fusion of Deep and Local Features Using Random Forests for Neonatal HIE Segmentation -- Enhancing Lesion Segmentation in the BONBID-HIE Challenge: An Ensemble Strategy -- An Ensemble Approach for Segmentation of Neonatal HIE lesions -- Improving Segmentation of Hypoxic Ischemic Encephalopathy Lesions by Heavy Data Augmentation: Contribution to the BONBID Challenge -- A Deep Neural Network Approach for the Lesion Segmentation from Neonatal Brain Magnetic Resonance Imaging -- SegResNet based Reciprocal Transformation for BONBID-HIE Lesion Segmentation -- Trauma THOMPSON 2023 -- Overview of the Trauma THOMPSON Challenge at MICCAI 2023 -- The Trauma THOMPSON Challenge Report MICCAI 2023 -- Action Recognition and Action Anticipation Tasks in the Trauma THOMPSON Challenge Technical Report -- QuIIL at T3 challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person View. |
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▼aThis book constitutes the proceedings of the First BONBID-HIE Lesion Segmentation Challenge and the First Trauma Thompson Challenge, held in conjunction with MICCAI 2023, in Vancouver, BC, Canada, during October 2023. For BONBID-HIE 2023 Challenge 6 papers have been accepted out of 14 submissions. They span a broad array of approaches leveraging anatomical information about HIE, data augmentation, training strategies, model architecture, and integration with traditional machine learning methods. For the TTC 2023 Trauma Thompson Challenge 4 accepted contributions are included in this book. They deal with advancements in machine learning methods and their practical applications in addressing small and diffuse lesions in HIE segmentation. . |
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▼aImage processing▼xDigital techniques. |
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▼aComputer vision. |
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▼aArtificial intelligence. |
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▼aComputer Imaging, Vision, Pattern Recognition and Graphics. |
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▼aArtificial Intelligence. |
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▼aBao, Rina.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aGrant, Ellen.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aKirkpatrick, Andrew.▼eeditor.▼0(orcid)0000-0002-4470-7089▼1https://orcid.org/0000-0002-4470-7089▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aWachs, Juan.▼eeditor.▼0(orcid)0000-0002-6425-5745▼1https://orcid.org/0000-0002-6425-5745▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aOu, Yangming.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt |
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▼aSpringerLink (Online service) |
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▼tSpringer Nature eBook |
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▼iPrinted edition:▼z9783031716256 |
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▼iPrinted edition:▼z9783031716270 |
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▼aLecture Notes in Computer Science,▼x1611-3349 ;▼v14567 |
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▼uhttps://doi.org/10.1007/978-3-031-71626-3 |
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▼aComputer Science (SpringerNature-11645) |
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▼aComputer Science (R0) (SpringerNature-43710) |