Medical Applications with Disentanglements

First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Herausgegeben:Fragemann, Jana; Li, Jianning; Liu, Xiao; Tsaftaris, Sotirios A.; Egger, Jan; Kleesiek, Jens

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 13823
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-25045-3
• 1st ed. 2023
• Seitenzahl: 140
• Erscheinungstermin: 1. Februar 2023
• Englisch
• Abmessung: 235mm x 155mm x 8mm
• Gewicht: 224g
• ISBN-13: 9783031250453
• ISBN-10: 3031250451
• Artikelnr.: 66886494

Inhaltsangabe

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 beta-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.