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245 1 0 _aLarge-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention
_h[electronic resource] :
_bInternational Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings /
_cedited 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.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXX, 154 p. 62 illus., 48 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
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347 _atext file
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11851
505 0 _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.
520 _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 inmedical 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.
650 0 _aComputer vision.
_9120815
650 0 _aArtificial intelligence.
_93407
650 0 _aMedical informatics.
_94729
650 1 4 _aComputer Vision.
_9120816
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aHealth Informatics.
_931799
700 1 _aZhou, Luping.
_eeditor.
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_9120817
700 1 _aHeller, Nicholas.
_eeditor.
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_9120818
700 1 _aShi, Yiyu.
_eeditor.
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_9120819
700 1 _aXiao, Yiming.
_eeditor.
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_9120820
700 1 _aSznitman, Raphael.
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_9120821
700 1 _aCheplygina, Veronika.
_eeditor.
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_9120822
700 1 _aMateus, Diana.
_eeditor.
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_9120823
700 1 _aTrucco, Emanuele.
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_9120824
700 1 _aHu, X. Sharon.
_eeditor.
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_9120825
700 1 _aChen, Danny.
_eeditor.
_4edt
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_9120826
700 1 _aChabanas, Matthieu.
_eeditor.
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_9120827
700 1 _aRivaz, Hassan.
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700 1 _aReinertsen, Ingerid.
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_9120829
710 2 _aSpringerLink (Online service)
_9120830
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030336417
776 0 8 _iPrinted edition:
_z9783030336431
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11851
_9120831
856 4 0 _uhttps://doi.org/10.1007/978-3-030-33642-4
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