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020 _a9783031188145
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024 7 _a10.1007/978-3-031-18814-5
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072 7 _aCOM016000
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245 1 0 _aMultiscale Multimodal Medical Imaging
_h[electronic resource] :
_bThird International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings /
_cedited by Xiang Li, Jinglei Lv, Yuankai Huo, Bin Dong, Richard M. Leahy, Quanzheng Li.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2022.
300 _aVIII, 131 p. 46 illus., 42 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13594
505 0 _aM^2F: Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis -- Visual Modalities based Multimodal Fusion for Surgical Phase Recognition -- Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images -- Vessel Segmentation via Link Prediction of Graph Neural Networks -- A Bagging Strategy-Based Multi-Scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset -- Liver Segmentation Quality Control in Multi-Sequence MR Studies -- Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI -- Gabor Filter-Embedded U-Net with Transformer-based Encoding for Biomedical Image Segmentation -- Learning-based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study -- Coordinate Translator for Learning Deformable Medical Image Registration -- Towards Optimal Patch Size in Vision Transformers forTumor Segmentation -- Improve Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT scans.
520 _aThis book constitutes the refereed proceedings of the Third International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with MICCAI 2022 in singapore, in September 2022. The 12 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.
650 0 _aComputer vision.
_9164821
650 0 _aArtificial intelligence.
_93407
650 0 _aComputers.
_98172
650 0 _aApplication software.
_9164822
650 1 4 _aComputer Vision.
_9164823
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputing Milieux.
_955441
650 2 4 _aComputer and Information Systems Applications.
_9164824
700 1 _aLi, Xiang.
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_9164825
700 1 _aLv, Jinglei.
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700 1 _aHuo, Yuankai.
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_9164827
700 1 _aDong, Bin.
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_9164828
700 1 _aLeahy, Richard M.
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_9164829
700 1 _aLi, Quanzheng.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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830 0 _aLecture Notes in Computer Science,
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-18814-5
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