LEADER 07117nam 22007815 450 001 996466295103316 005 20200902192021.0 010 $a3-030-33642-5 024 7 $a10.1007/978-3-030-33642-4 035 $a(CKB)4100000009844989 035 $a(DE-He213)978-3-030-33642-4 035 $a(MiAaPQ)EBC5982923 035 $a(PPN)257357815 035 $a(EXLCZ)994100000009844989 100 $a20191120d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLarge-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention$b[electronic resource] $eInternational Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings /$fedited by Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, X. Sharon Hu, Danny Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XX, 154 p. 62 illus., 48 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11851 311 $a3-030-33641-7 320 $aIncludes bibliographical references and index. 327 $a4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019) -- Comparison of active learning strategies applied to lung nodule segmentation in CT scans -- Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation -- XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis -- Data Augmentation based on Substituting Regional MRI Volume Scores -- Weakly supervised segmentation from extreme points -- Exploring the Relationship between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks -- DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs -- The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018 -- First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019) -- Hardware Acceleration of Persistent Homology Computation -- Deep Compressed Pneumonia Detection for Low-Power Embedded Devices -- D3MC: A Reinforcement Learning based Data-driven Dyna Model Compression -- An Analytical Method of Automatic Alignment for Electron Tomography -- Fixed-Point U-Net Quantization for Medical Image Segmentation -- Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019) -- Registration of ultrasound volumes based on Euclidean distance transform -- Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set -- Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions. 330 $aThis book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications in medical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11851 606 $aOptical data processing 606 $aArtificial intelligence 606 $aHealth informatics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aHealth informatics. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aHealth Informatics. 676 $a006.6 676 $a006.37 702 $aZhou$b Luping$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHeller$b Nicholas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShi$b Yiyu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aXiao$b Yiming$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSznitman$b Raphael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCheplygina$b Veronika$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMateus$b Diana$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTrucco$b Emanuele$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHu$b X. Sharon$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChen$b Danny$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChabanas$b Matthieu$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRivaz$b Hassan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aReinertsen$b Ingerid$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Workshop, LABELS$f(2019), 712 12$aInternational Workshop, HAL-MICCAI$f(2019), 712 12$aInternational Workshop, CuRIOUS$f(2019), 712 12$aInternational Workshop, MICCAI$f(2019 :$eShenzhen, China), 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466295103316 996 $aLarge-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention$92501924 997 $aUNISA