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Medical Applications with Disentanglements [electronic resource] : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / edited by Jana Fragemann, Jianning Li, Xiao Liu, Sotirios A. Tsaftaris, Jan Egger, Jens Kleesiek.

Contributor(s): Fragemann, Jana [editor.] | Li, Jianning [editor.] | Liu, Xiao [editor.] | Tsaftaris, Sotirios A [editor.] | Egger, Jan [editor.] | Kleesiek, Jens [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 13823Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: X, 127 p. 40 illus., 26 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031250460.Subject(s): 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 ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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 reviewed and cover generative adversarial networks (GAN), variational autoencoders (VAE) and normalizing-flow architectures as well as a wide range of medical applications, like brain age prediction, skull reconstruction and unsupervised pathology disentanglement.
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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.

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 reviewed and cover generative adversarial networks (GAN), variational autoencoders (VAE) and normalizing-flow architectures as well as a wide range of medical applications, like brain age prediction, skull reconstruction and unsupervised pathology disentanglement.

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