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Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a

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書目名稱Machine Learning in Medical Imaging
副標(biāo)題14th International W
編輯Xiaohuan Cao,Xuanang Xu,Xi Ouyang
視頻videohttp://file.papertrans.cn/621/620675/620675.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a
描述The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023.?. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on?major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc..
出版日期Conference proceedings 2024
關(guān)鍵詞artificial intelligence; bioinformatics; computer networks; computer science; computer systems; computer
版次1
doihttps://doi.org/10.1007/978-3-031-45673-2
isbn_softcover978-3-031-45672-5
isbn_ebook978-3-031-45673-2Series 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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,Robust Unsupervised Super-Resolution of?Infant MRI via?Dual-Modal Deep Image Prior,d subject compliance, high-quality infant MRI can be challenging. Without imposing additional burden on image acquisition, image super-resolution (SR) can be used to enhance image quality post-acquisition. Most SR techniques are supervised and trained on multiple aligned low-resolution (LR) and high
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