Dynamic Attribute-guided Few-shot Open-set Network for medical image diagnosis

Abstract

The scarcity of data on rare diseases poses a significant challenge to the development of diagnostic systems. While few-shot learning (FSL) offers promise in low-data regimes, it often struggles in open-set scenarios, failing to identify unknown diseases. In this paper, we introduce a Dynamic Attribute-guided Few-shot Open-set Network (DAFON), representing the first effort to simultaneously address closed-set classification and open-set recognition in rare disease diagnosis. To alleviate incomprehensive category knowledge stemming from data scarcity, we propose a Global Attribute Generator to create attributes and produce image attribute activations for closed-set data as auxiliary information. An attribute space is then constructed and employed to generate the pseudo open-set attribute activations using a designed Open-set Data Sampler. By incorporating closed-set and open-set attribute activations as conditions, we propose a Dynamic Attribute Guided Alignment module to align feature space with attribute space, so as to derive feature space with intra-class compactness for closed-set and open-set classes. Our DAFON achieves state-of-the-art performance on two public medical image datasets, demonstrating its effectiveness for FSOSR in medical image diagnosis.