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Medical Applications with Disentanglements First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings /

This book constitutes the post-conference proceedings of the First MICCAI Workshop on Medical Applications with Disentanglements, MAD 2022, held in conjunction with MICCAI 2022, in Singapore, on September22, 2022. The 8 full papers presented in this book together with one short paper were carefully...

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Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Fragemann, Jana (Editor), Li, Jianning (Editor), Liu, Xiao (Editor), Tsaftaris, Sotirios A. (Editor), Egger, Jan (Editor), Kleesiek, Jens (Editor)
Formato: Electrónico eBook
Lenguaje:inglés
Publicado: Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Edición:1st ed. 2023.
Colección:Lecture Notes in Computer Science, 13823
Materias:
Image processing > Digital techniques.
Computer vision.
Artificial intelligence.
Computer engineering.
Computer networks .
Computers.
Application software.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Vision.
Artificial Intelligence.
Computer Engineering and Networks.
Computing Milieux.
Computer and Information Systems Applications.
Acceso en línea:https://doi.org/10.1007/978-3-031-25046-0
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Tabla de Contenidos:
  • Applying Disentanglement in the Medical Domain: An Introduction
  • HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
  • Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs
  • Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations
  • Instance-Specific Augmentation of Brain MRIs with Variational Autoencoder
  • Low-rank and Sparse Metamorphic Autoencoders for Unsupervised Pathology Disentanglement
  • Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
  • Disentangling Factors of Morpholigical Variation in an Invertible Brain Aging Model
  • A study of representational properties of unsupervised anomaly detection in brain MRI.

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