Structure Guided Manifolds for Discovery of Disease Characteristics. (arXiv:2209.11015v2 [eess.IV] UPDATED)

In medical image analysis, the subtle visual characteristics of many diseases
are challenging to discern, particularly due to the lack of paired data. For
example, in mild Alzheimer's Disease (AD), brain tissue atrophy can be
difficult to observe from pure imaging data, especially without paired AD and
Cognitively Normal ( CN ) data for comparison. This work presents Disease
Discovery GAN ( DiDiGAN), a weakly-supervised style-based framework for
discovering and visualising subtle disease features. DiDiGAN learns a disease
manifold of …

arxiv discovery disease