DualSS: A Dual‐Diffusion Synthetic Sampling Framework for Screening Fatty Liver Severity With Class‐Imbalanced Tongue Images

Fatty liver disease (FLD) is a prevalent chronic condition that can progress to clinically significant liver injury if left untreated. Given its high prevalence, FLD poses a substantial public health burden. Traditional Chinese medicine suggests that the appearance of tongue reflects hepatic and metabolic status, enabling artificial intelligence‐based tongue diagnosis to provide rapid low‐cost prescreening for FLD severity. In practice, the development of high‐quality models is hindered by costly disease labelling, scarcity of positive cases, and severe class imbalance. In this work, we propose DualSS, a dual‐diffusion synthetic sampling framework to mitigate these limitations. DualSS first trains a disease‐agnostic dual‐diffusion generator (DDG) on tongue‐image data, leveraging abundant negative samples to learn unified tongue representations. Building on DDG, we design a latent space synthetic sampling pipeline that synthesises realistic positive‐class images to construct a more balanced augmented training dataset. We then couple this pipeline with a noise‐robust training strategy on the augmented dataset to further enhance the learning of downstream diagnostic models. Extensive experiments on FLD severity classification using tongue images and physiological indicators show that DualSS consistently outperforms existing rebalancing baselines, offering an effective and generalisable solution to alleviate positive sample scarcity in tongue image‐based medical diagnosis.