LEADER 04189nam 22006135 450 001 9910366656503321 005 20200702034301.0 010 $a3-030-37969-8 024 7 $a10.1007/978-3-030-37969-8 035 $a(CKB)5280000000190070 035 $a(MiAaPQ)EBC6111464 035 $a(DE-He213)978-3-030-37969-8 035 $a(PPN)242818633 035 $a(EXLCZ)995280000000190070 100 $a20191219d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultiscale Multimodal Medical Imaging $eFirst International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /$fedited by Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (x, 108 pages) $cillustrations 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11977 311 $a3-030-37968-X 327 $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. 330 $aThis book constitutes the refereed proceedings of the First International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11977 606 $aOptical data processing 606 $aMachine learning 606 $aPattern recognition 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aOptical data processing. 615 0$aMachine learning. 615 0$aPattern recognition. 615 14$aImage Processing and Computer Vision. 615 24$aMachine Learning. 615 24$aPattern Recognition. 676 $a616.0754028 676 $a616.0754 (edition:23) 702 $aLi$b Quanzheng$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLeahy$b Richard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDong$b Bin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Xiang$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366656503321 996 $aMultiscale Multimodal Medical Imaging$92073402 997 $aUNINA