Due to the frequent co-occurrence of multiple diseases in patients, automated multi-label diagnosis (MLD) of medical images remains a challenging yet clinically important task, particularly due to two major challenges: (1) feature confusion arises from the loss of disease localization information due to pooling operations, and (2) suboptimal probabilistic modeling capacity and feature expressiveness using vanilla MLPs (multi-layer perceptrons) as the predictors. In this work, we propose a novel MLD framework that integrates a Spatial-Disease Feature Condensation (SDFC) mechanism and Kolmogorov-Arnold Layers (KALs). The SDFC module preserves the full spatial resolution of image features by condensing channel-wise information into disease-aligned spatial representations . These spatial-disease features are subsequently processed by a KALs-based classifier, which offers enhanced expressiveness for modeling complex interdependencies among co-occurring diseases. Extensive experiments conducted on two publicly available clinical datasets, ODIR and NIH ChestX-ray, demonstrate that the proposed method consistently outperforms state-of-the-art MLD approaches in terms of diagnostic performance, generalization, and reliability. By explicitly preserving spatial disease information and leveraging the superior function modeling capability of Kolmogorov-Arnold, the proposed framework provides an effective and robust solution for multi-label medical image diagnosis, with potential value for clinical decision support systems.
Yuan et al. (Fri,) studied this question.