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Titlebook: Medical Image Understanding and Analysis; 26th Annual Conferen Guang Yang,Angelica Aviles-Rivero,Carola-Bibiane S Conference proceedings 20

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發(fā)表于 2025-3-21 16:26:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Medical Image Understanding and Analysis
副標(biāo)題26th Annual Conferen
編輯Guang Yang,Angelica Aviles-Rivero,Carola-Bibiane S
視頻videohttp://file.papertrans.cn/630/629284/629284.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Medical Image Understanding and Analysis; 26th Annual Conferen Guang Yang,Angelica Aviles-Rivero,Carola-Bibiane S Conference proceedings 20
描述.This book constitutes the refereed proceedings of the 26th Conference on Medical Image Understanding and Analysis, MIUA 2022, held in Cambridge, UK, in July 2022. ..The 65 full papers presented were carefully reviewed and selected from 95 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging..Chapter “FCN-Transformer Feature Fusion for Polyp Segmentation” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; classification methods; color image processing; computer networks; computer sys
版次1
doihttps://doi.org/10.1007/978-3-031-12053-4
isbn_softcover978-3-031-12052-7
isbn_ebook978-3-031-12053-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Multimodal Cardiomegaly Classification with?Image-Derived Digital Biomarkers-derived digital biomarkers, the cardiothoracic ratio (CTR) and the cardiopulmonary area ratio (CPAR). The CTR and CPAR values are estimated using segmentation and detection models. These are then integrated into a multimodal network trained simultaneously on chest radiographs and ICU data (vital si
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Proton Density Fat Fraction of?Breast Adipose Tissue: Comparison of?the?Effect of?Fat Spectra and?Inmarker which has not yet been thoroughly examined in the characterisation of breast fat; this work therefore explores the estimation of breast-specific PDFF. An MR spectrum derived from healthy breast fat is shown to perform significantly better in PDFF calculation of breast adipose tissue amongst a
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Revisiting the?Shape-Bias of?Deep Learning for?Dermoscopic Skin Lesion Classificationrowing body of work aiming to align deep models’ decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily expected to improve the robustness of these models under covariate shift. In this paper, we revisit the significance of . for the cl
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Faster Diffusion Cardiac MRI with?Deep Learning-Based Breath Hold Reductionvo and non-invasively, which no other imaging modality allows. This innovative technology could revolutionise the ability to perform cardiac clinical diagnosis, risk stratification, prognosis and therapy follow-up. However, DT-CMR is currently inefficient with over six minutes needed to acquire a si
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