AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis

Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. Howev...

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Vydané v:	International journal of computer vision Ročník 131; číslo 8; s. 1995 - 2014
Hlavní autori:	Cao, Bing, Bi, Zhiwei, Hu, Qinghua, Zhang, Han, Wang, Nannan, Gao, Xinbo, Shen, Dinggang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2023 Springer Springer Nature B.V
Predmet:	Artificial Intelligence Coders Collaborative learning Computer Imaging Computer Science Computer vision Group work in education Image Processing and Computer Vision Image reconstruction Learning Medical imaging Medical imaging equipment Missing data Pattern Recognition Pattern Recognition and Graphics Source code Synthesis Target masking Team learning approach in education Vision Medical image synthesis Self-representation Autoencoder Multimodal collaborative learning
ISSN:	0920-5691, 1573-1405
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