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Titlebook: Medical Image Understanding and Analysis; 24th Annual Conferen Bartłomiej W. Papież,Ana I. L. Namburete,J. Alison Conference proceedings 20

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书目名称Medical Image Understanding and Analysis
副标题24th Annual Conferen
编辑Bartłomiej W. Papież,Ana I. L. Namburete,J. Alison
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Medical Image Understanding and Analysis; 24th Annual Conferen Bartłomiej W. Papież,Ana I. L. Namburete,J. Alison Conference proceedings 20
描述This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. .The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: ​image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling..
出版日期Conference proceedings 2020
关键词artificial intelligence; bioinformatics; color image processing; color images; computer systems; computer
版次1
doihttps://doi.org/10.1007/978-3-030-52791-4
isbn_softcover978-3-030-52790-7
isbn_ebook978-3-030-52791-4Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Segmenting Hepatocellular Carcinoma in Multi-phase CTapply input-level fusion for stacks of multi-phase data as channel input. Finally, we make use of a public single-phase CT liver tumour dataset for the pre-training of network parameters to improve the generalisation capabilities of our networks.
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On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images presence of low contrast, when given suitable user input. In addition, we implement a deep learning algorithm based on this model, allowing for a supervised, semi-supervised or unsupervised approach, depending on data availability.
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Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentationxpect that the proposed training scheme would be applicable to any feedforward network and task. We show that the network can be used to harmonise two datasets and also show that the network is applicable in the common scenario of limited available training data, meaning that the network should be applicable for real-world segmentation problems.
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