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

• Published in: Lecture Notes in Computer Science Vol. 11851
• Main Authors: Zhou, Luping, Heller, Nicholas, Shi, Yiyu, Xiao, Yiming, Sznitman, Raphael, Cheplygina, Veronika, Mateus, Diana, Trucco, Emanuele, Hu, X. Sharon, Chen, Danny
• Format: eBook Conference Proceeding
• Language: English
• Published: Cham Springer Nature 2019; Springer International Publishing AG; Springer International Publishing; Springer
• Edition: 1
• Series: Lecture Notes in Computer Science
• Subjects: Artificial Intelligence; Bioengineering; Biomedical engineering-Congresses; Computer programming, programs, data; Computer Science; Graphics; Health Informatics; Image Processing; Image Processing and Computer Vision; Imaging; Life Sciences; Medical Imaging; medical images; deep learning; image quality; image segmentation; image compression; neural networks; hardware-aided diagnosis; hardware-assisted intervention; image analysis; artificial intelligence; medical imaging
• ISBN: 9783030336424; 3030336425; 9783030336417; 3030336417
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-33642-4

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.