Contrastive Analysis (CA) regards the problem of identifying patterns in
images that allow distinguishing between a background (BG) dataset (i.e.
healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent
works on this topic rely on variational autoencoders (VAE) or contrastive
learning strategies to learn the patterns that separate TG samples from BG
samples in a supervised manner. However, the dependency on target (unhealthy)
samples can be challenging in medical scenarios due to their limited
availability. Also, the blurred reconstructions of VAEs lack utility and
interpretability. In this work, we redefine the CA task by employing a
self-supervised contrastive encoder to learn a latent representation encoding
only common patterns from input images, using samples exclusively from the BG
dataset during training, and approximating the distribution of the target
patterns by leveraging data augmentation techniques. Subsequently, we exploit
state-of-the-art generative methods, i.e. diffusion models, conditioned on the
learned latent representation to produce a realistic (healthy) version of the
input image encoding solely the common patterns. Thorough validation on a
facial image dataset and experiments across three brain MRI datasets
demonstrate that conditioning the generative process of state-of-the-art
generative methods with the latent representation from our self-supervised
contrastive encoder yields improvements in the generated image quality and in
the accuracy of image classification. The code is available at
this https URL

通过使用自我监督对比编码器学习仅从输入图像中提取共同模式的潜在表示，利用数据增强技术近似目标模式的分布，并利用高级生成方法的条件生成过程，生成仅包含共同模式的真实（健康）版本的输入图像，从而提高生成图像质量和图像分类准确性。