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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

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書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
副標(biāo)題23rd International C
編輯Anne L. Martel,Purang Abolmaesumi,Leo Joskowicz
視頻videohttp://file.papertrans.cn/630/629199/629199.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow
描述.The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic...The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:..Part I: machine learning methodologies..Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks..Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis..Part IV: segmentation; shape models and landmark detection..Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology..Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal im
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer aided diagnosis; computer vision; image analysis; image processing; ima
版次1
doihttps://doi.org/10.1007/978-3-030-59728-3
isbn_softcover978-3-030-59727-6
isbn_ebook978-3-030-59728-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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978-3-030-59727-6Springer Nature Switzerland AG 2020
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https://doi.org/10.1007/978-3-030-59728-3artificial intelligence; computer aided diagnosis; computer vision; image analysis; image processing; ima
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Parkinson’s Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networks after employing an SVM classifier, achieve a sensitivity of disease detection of 94% – comparable to approaches that normally require whole-brain analysis. To the best of our knowledge, this is the first study that employs brainstem functional sub-regions for Parkinson’s disease detection.
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