WDFNet: Adaptive Frequency Guidance for Diffusion Based Ultrasound Image Classification

Existing label diffusion classifiers rely primarily on deep semantic image conditioning, in which frequency information is learned only implicitly. Although deep networks can capture frequency dependent patterns, prior studies have reported frequency biases in learned representations and shown that explicit frequency modeling can complement conventional spatial features in visual and medical image analysis. This issue is particularly relevant to ultrasound, where high frequency responses may represent fine anatomical boundaries and tissue texture, but may also reflect speckle related patterns and acquisition variation. We propose WDFNet, an adaptive frequency conditioning framework for label diffusion classification that explicitly introduces wavelet reconstructed high frequency evidence into the image guidance pathway. Its Adaptive Frequency Guide (AFG) selects complementary frequency responses according to semantic context, while its Frequency Alignment Module (FAM) adjusts the fused condition according to the current noisy label, diffusion timestep, and auxiliary class prior. Experiments on six medical image datasets show consistent improvements over the evaluated discriminative and diffusion based classifiers. Repeated runs, capacity matched controls, fixed fusion controls, paired label noise analyses, and controlled corruption experiments further support the contribution of adaptive frequency conditioning rather than label diffusion or additional branch capacity alone.