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Showing 1 - 8 of
8 matches in All Departments
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Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings (Paperback, 1st ed. 2022)
Nicholas Heller, Fabian Isensee, Darya Trofimova, Resha Tejpaul, Nikolaos Papanikolopoulos, …
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R1,672
Discovery Miles 16 720
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Ships in 10 - 15 working days
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This book constitutes the Second International Challenge on Kidney
and Kidney Tumor Segmentation, KiTS 2021, which was held in
conjunction with the 24th International Conference on Medical Image
Computing and Computer-Assisted Intervention, MICCAI 2021. The
challenge took place virtually on September 27, 2021, due to the
COVID-19 pandemic. The 21 contributions presented were carefully
reviewed and selected from 29 submissions. This challenge aims to
develop the best system for automatic semantic segmentation of
renal tumors and surrounding anatomy.
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Deep Generative Models, and Data Augmentation, Labelling, and Imperfections - First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (Paperback, 1st ed. 2021)
Sandy Engelhardt, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, …
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R1,969
Discovery Miles 19 690
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the First MICCAI
Workshop on Deep Generative Models, DG4MICCAI 2021, and the First
MICCAI Workshop on Data Augmentation, Labelling, and Imperfections,
DALI 2021, held in conjunction with MICCAI 2021, in October 2021.
The workshops were planned to take place in Strasbourg, France, but
were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021
accepted 12 papers from the 17 submissions received. The workshop
focusses on recent algorithmic developments, new results, and
promising future directions in Deep Generative Models. Deep
generative models such as Generative Adversarial Network (GAN) and
Variational Auto-Encoder (VAE) are currently receiving widespread
attention from not only the computer vision and machine learning
communities, but also in the MIC and CAI community. For DALI 2021,
15 papers from 32 submissions were accepted for publication. They
focus on rigorous study of medical data related to machine learning
systems.
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Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings (Paperback, 1st ed. 2020)
Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, …
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R1,587
Discovery Miles 15 870
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Ships in 10 - 15 working days
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This book constitutes the refereed joint proceedings of the Third
International Workshop on Interpretability of Machine Intelligence
in Medical Image Computing, iMIMIC 2020, the Second International
Workshop on Medical Image Learning with Less Labels and Imperfect
Data, MIL3ID 2020, and the 5th International Workshop on
Large-scale Annotation of Biomedical data and Expert Label
Synthesis, LABELS 2020, held in conjunction with the 23rd
International Conference on Medical Imaging and Computer-Assisted
Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8
full papers presented at iMIMIC 2020, 11 full papers to MIL3ID
2020, and the 10 full papers presented at LABELS 2020 were
carefully reviewed and selected from 16 submissions to iMIMIC, 28
to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on
introducing the challenges and opportunities related to the topic
of interpretability of machine learning systems in the context of
medical imaging and computer assisted intervention. MIL3ID deals
with best practices in medical image learning with label scarcity
and data imperfection. The LABELS papers present a variety of
approaches for dealing with a limited number of labels, from
semi-supervised learning to crowdsourcing.
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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 (Paperback, 1st ed. 2019)
Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, …
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R1,580
Discovery Miles 15 800
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Ships in 10 - 15 working days
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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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