Feature Learning Using Stacked Autoencoder for Shared and Multimodal Fusion of Medical Images
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Stacked-autoencoder-based multimodal systems compress each modality separately in the encoder and build a shared latent representation in the decoder without supervision.
“the authors of [210–212] designed multimodal systems based on SAEs, where the encoder side of the architecture represents and compresses each unimodal feature separately, and the decoder side constructs the latent (shared) representation of the inputs in a unsupervised manner.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [211]
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- Authors
- Singh, V., Verma, N. K., Ul Islam, Z., & Cui, Y.
- Affiliations
- Indian Institute of Technology Kanpur (IN)
- University of Tennessee Health Science Center (US)
- Corresponding author
- Singh, Vikas
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Singapore
- Published
- 2018-08-01
- Pages
- 53-66
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W2886816990
- Cited by
- 13
- References
- 38
- Citations by year
- 2026: 1 · 2025: 2 · 2021: 4 · 2020: 3 · 2019: 3
- FWCI
- 5.42
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- stacked autoencoder, representation learning, multimodal fusion, shared learning, medical image analysis, deep learning, automatic feature extraction
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