Test-Time Adaptation through Semantically-guided Feature Decomposition for Few-shot Chest X-ray Diagnosis

Training a deep neural network with a small amount of labeled data is challenging. The challenge is even more severe for medical images because of the many possible variations in the images. We propose a novel framework for few-shot chest x-ray (CXR) diagnosis. For classification problems, training with limited data may be facilitated if class-specific features can be extracted and utilized. Semantic information about the abnormalities may also be helpful in this context. To that end, we design an autoencoder-based approach that extracts visual features and decomposes them into class-agnostic and class-specific features utilizing the semantic information of the abnormalities. The decomposition helps in efficient classification using the class-specific features. Additionally, we perform test-time adaptation to deal with possible variations in the test data compared to the training data. From this perspective, our method is one of the first of its kind. Extensive evaluations on publicly available chest x-ray datasets under few-shot settings show the effectiveness of our method. Results on the publicly available chest x-ray datasets show a 3–5\% improvement in AUROC scores.