Cited by 13Advanced Image Fusion TechniquesMultimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Feature Learning Using Stacked Autoencoder for Shared and Multimodal Fusion of Medical Images

Singh, Vikas, Verma, Nishchal K., Ul Islam, Zeeshan, Cui, Yan·2019·Advances in Intelligent Systems and Computing

Integrity signals

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Crossref · 10.1007/978-981-13-1132-1_5

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38 of 38 references verified0 citation errors · 0 not checkable

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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.”

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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
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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