The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis
Open accessAbstract
Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multi-modality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes the form of convolutional neural networks, where the input and output are two volumetric modalities. The network contains a large number of trainable parameters that capture the relationship between input and output modalities. When trained on subjects with all modalities, the network can estimate the output modality given the input modality. We evaluated our method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, where the input and output modalities are MRI and PET images, respectively. Results showed that our method significantly outperformed prior methods.
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Self-citationMitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
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The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
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Metadata
- Authors
- Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., & Ji, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Volume
- 17
- Issue
- Pt 3
- Pages
- 305-312
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W66427752
- Cited by
- 469
- References
- 11
- FWCI
- 87.24
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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