Hyperbolic Geometry-Driven Robustness Enhancement for Rare Skin Disease Diagnosis

The automated diagnosis of rare skin diseases using dermoscopy images, known as a few-shot learning (FSL) problem, remains challenging, since traditional FSL research tends to disregard the intrinsic hierarchical nature of rare diseases and data uncertainty. To address these issues, we propose to conduct rare skin disease diagnosis in hyperbolic space, which facilitates implicit class hierarchical structures and precise uncertainty measurement due to pivotal geometrical properties. We propose a Hyperbolic Geometry-driven Robustness Enhancement (HGRE) framework specifically tailored for diagnosing rare skin diseases. The HGRE framework uses implicit hierarchical relation in the hyperbolic space to better represent the features of rare diseases. Moreover, the framework incorporates an Adversarial Proxy Construction (APC) module to address the problem of data uncertainty. Specifically, the APC module uses the distance to the hyperbolic space origin as an indicator of uncertainty to filter and construct adversarial proxies for each uncertain prototype to achieve adversarial robust training. Leveraging the two unique geometrical properties, our HGRE framework effectively addresses the limitations of insufficient hierarchical relation utilization and data uncertainty in FSL-based rare skin disease diagnosis. This enhancement of the model's robustness in training has been corroborated by extensive empirical validation on two skin lesion datasets, where HGRE's performance notably surpassed existing state-of-the-art FSL methods.