Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images

doi:doi:10.57702/khheezt8

Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images

The proposed framework is composed of two networks (see Fig 1). The encoder network q with parameters of φ computes qφ(z|x) : xi → zi. The encoder maps an input image xi ∈ X to its latent embedding zi ∈ Z in a lower dimensionality space compared to the input space X. The decoder network p parametrizes by θ, pθ : zi → x(cid:48)i, and reconstructs xi from its latent embedding zi.

BibTex:

@dataset{Farzin_Soleymani_and_Mohammad_Eslami_and_Tobias_Elze_and_Bernd_Bischl_and_Mina_Rezaei_2024,
    abstract = {The proposed framework is composed of two networks (see Fig 1). The encoder network q with parameters of φ computes qφ(z|x) : xi → zi. The encoder maps an input image xi ∈ X to its latent embedding zi ∈ Z in a lower dimensionality space compared to the input space X. The decoder network p parametrizes by θ, pθ : zi → x(cid:48)i, and reconstructs xi from its latent embedding zi.},
    author = {Farzin Soleymani and Mohammad Eslami and Tobias Elze and Bernd Bischl and Mina Rezaei},
    doi = {10.57702/khheezt8},
    institution = {No Organization},
    keyword = {'Clustering', 'Medical Images', 'Variational Autoencoders'},
    month = {dec},
    publisher = {TIB},
    title = {Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images},
    url = {https://service.tib.eu/ldmservice/dataset/deep-variational-clustering-framework-for-self-labeling-of-large-scale-medical-images},
    year = {2024}
}