Medical Applications with Disentanglements
Springer International Publishing (Verlag)
978-3-031-25045-3 (ISBN)
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.
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.
| Erscheinungsdatum | 03.02.2023 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | X, 127 p. 40 illus., 26 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 225 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Schlagworte | Applications • Artificial Intelligence • Bioinformatics • color image processing • color images • Computer Science • Computer Security • conference proceedings • Correlation Analysis • Data Security • Digital image • Engineering • Factorization • Image Analysis • Image Processing • image reconstruction • Image Segmentation • Informatics • learning • machine learning • Mathematics • Matrix Algebra • Medical Images • Research • Signal Processing |
| ISBN-10 | 3-031-25045-1 / 3031250451 |
| ISBN-13 | 978-3-031-25045-3 / 9783031250453 |
| Zustand | Neuware |
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