Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware imbalance correction.

Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging phenotype classification of rare diseases is challenging due to the severe shortage of training examples. Few-shot learning (FSL) methods tackle this challenge by extracting generalizable prior knowledge from a large base dataset of common diseases and normal controls and transferring the knowledge to rare diseases. Yet, most existing methods require the base dataset to be labeled and do not make full use of the precious examples of rare diseases. In addition, the extremely small size of the training samples may result in inter-class performance imbalance due to insufficient sampling of the true distributions. To this end, we propose in this work a novel hybrid approach to rare disease imaging phenotype classification, featuring three key novelties targeted at the above drawbacks. First, we adopt the unsupervised representation learning (URL) based on self-supervising contrastive loss, whereby to eliminate the overhead in labeling the base dataset. Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases, composing a hybrid approach taking advantage of both unsupervised and (pseudo-) supervised learning on the base dataset. Third, we use the feature dispersion to assess the intra-class diversity of training samples, to alleviate the inter-class performance imbalance via dispersion-aware correction. Experimental results of imaging phenotype classification of both simulated (skin lesions and cervical smears) and real clinical rare diseases (retinal diseases) show that our hybrid approach substantially outperforms existing FSL methods (including those using a fully supervised base dataset) via effective integration of the URL, pseudo-label driven self-distillation, and dispersion-aware imbalance correction, thus establishing a new state of the art. • A simple yet effective approach to rare disease classification. • Eliminate the heavy burden of labeling the large base dataset. • Surprisingly superior performance to comparable supervised approaches. • Hybrid unsupervised and pseudo-label supervised self-distillation. • Dispersion-aware correction of inter-rare-disease performance imbalance. [ABSTRACT FROM AUTHOR]