Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
154
Utgivningsdatum
2019-11-21
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Heller, Nicholas / Shi, Yiyu
Illustrationer
48 Illustrations, color; 14 Illustrations, black and white; XX, 154 p. 62 illus., 48 illus. in color
Dimensioner
234 x 156 x 10 mm
Vikt
254 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030336417
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (häftad)

Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention

International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

Häftad Engelska, 2019-11-21
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This 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.
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4th 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.