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020 _a9783030379698
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024 7 _a10.1007/978-3-030-37969-8
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245 1 0 _aMultiscale Multimodal Medical Imaging
_h[electronic resource] :
_bFirst International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /
_cedited by Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aX, 109 p. 55 illus., 46 illus. in color.
_bonline resource.
336 _atext
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338 _aonline resource
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11977
505 0 _aMulti-Modal Image Prediction via Spatial Hybrid U-Net -- Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network -- OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images -- Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data -- Feature Pyramid based Attention for Cervical Image Classification -- Single-scan Dual-tracer Separation Network Based on Pre-trained GRU -- PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation -- Automated Classification of Arterioles and Venules for Retina Fundus Images using Dual Deeply-Supervised Network -- Liver Segmentation from Multimodal Images using HED-Mask R-CNN -- aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection -- Speckle Noise Removal in Ultrasound Images Using A Deep Convolutional Neural Network and A Specially Designed Loss Function -- Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video -- U-Net Training with Instance-Layer Normalization.
520 _aThis book constitutes the refereed proceedings of the First International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 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.
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650 0 _aMachine learning.
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650 0 _aPattern recognition systems.
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650 1 4 _aComputer Vision.
_9118452
650 2 4 _aMachine Learning.
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650 2 4 _aAutomated Pattern Recognition.
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700 1 _aLi, Quanzheng.
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700 1 _aLeahy, Richard.
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700 1 _aDong, Bin.
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700 1 _aLi, Xiang.
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776 0 8 _iPrinted edition:
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830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
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856 4 0 _uhttps://doi.org/10.1007/978-3-030-37969-8
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