Attention-Based Few-Shot Diagnosis of Chest X-Rays Using Semantic Signatures

Abstract

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Few-shot learning (FSL) in medical image analysis presents a formidable challenge, primarily owing to the scarcity of labeled data. We propose a few-shot learning approach for the diagnosis of chest x-rays. Our method first leverages the use of an attention mechanism for better feature extraction from chest x-rays. Subsequently, we exploit auxiliary information about various abnormalities found in chest x-rays. The auxiliary information in the form of semantic signatures guides the few-shot learning process for the diagnosis of chest x-rays. We evaluate the proposed approach on multiple publicly available chest x-ray datasets. Experimental results show as high as $8 \%$ performance improvement compared to several state-of-the-art FSL approaches. Our code can be found at this link https://github.com/dpmaharathy/ICIP-ATTENTIONBASED-FEW-SHOT-DIAGNOSIS-OF-CHEST-X-RAYSUSING-SEMANTIC-SIGNATURES.git.