  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 AD and CN visual characteristics, and the style codes sampled from
this manifold are imposed onto an anatomical structural "blueprint" to
synthesise paired AD and CN magnetic resonance images (MRIs). To suppress
non-disease-related variations between the generated AD and CN pairs, DiDiGAN
leverages a structural constraint with cycle consistency and anti-aliasing to
enforce anatomical correspondence. When tested on the Alzheimer's Disease
Neuroimaging Initiative ( ADNI) dataset, DiDiGAN showed key AD characteristics
(reduced hippocampal volume, ventricular enlargement, and atrophy of cortical
structures) through synthesising paired AD and CN scans. The qualitative
results were backed up by automated brain volume analysis, where systematic
pair-wise reductions in brain tissue structures were also measured
