Titlebook: Medical Applications with Disentanglements; First MICCAI Worksho Jana Fragemann,Jianning Li,Jens Kleesiek Conference proceedings 2023 The E

OASIS

增长

HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual IfoGAN is a popular disentanglement framework that learns unsupervised disentangled representations by maximising the mutual information between latent representations and their corresponding generated images. Maximisation of mutual information is achieved by introducing an auxiliary network and trai

COLIC

Veneer

exorbitant

Instance-Specific Augmentation of Brain MRIs with Variational Autoencodersowever, a typical spatial augmentation scheme is built upon ad hoc selections of spatial transformation parameters which are not determined by the data set and therefore may not capture spatial variations in the data. For segmentation networks trained in the low-data regime, these ad hoc transformat

agonist

invulnerable

CRUE

Disentangling Factors of Morphological Variation in an Invertible Brain Aging Modeln models that estimate a brain’s biological age using structural MR images, generative models that capture the conditional distribution of aging-related brain morphology changes, and hybrid generative-inferential models that handle both tasks. Generative models are useful when systematically analyzi

使显得不重要

hematuria