Medical Applications with Disentanglements [electronic resource] : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings /
Material type: TextSeries: Lecture Notes in Computer Science ; 13823Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023Description: X, 127 p. 40 illus., 26 illus. in color. online resourceContent type:- text
- computer
- online resource
- 9783031250460
- 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
- 006 23
- TA1501-1820
- TA1634
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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