TY  - BOOK
AU  - Fragemann,Jana
AU  - Li,Jianning
AU  - Liu,Xiao
AU  - Tsaftaris,Sotirios A.
AU  - Egger,Jan
AU  - Kleesiek,Jens
ED  - SpringerLink (Online service)
TI  - Medical Applications with Disentanglements: First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9783031250460
AV  - TA1501-1820 
U1  - 006 23
PY  - 2023///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Image processing
KW  - Digital techniques
KW  - Computer vision
KW  - Artificial intelligence
KW  - Computer engineering
KW  - Computer networks 
KW  - Computers
KW  - Application software
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
KW  - Computer Vision
KW  - Artificial Intelligence
KW  - Computer Engineering and Networks
KW  - Computing Milieux
KW  - Computer and Information Systems Applications
N1  - Acceso multiusuario; 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-25046-0
ER  -