Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays

TL;DR

This paper introduces GenCDML-FSL, a comprehensive framework for multi-label, cross-domain, few-shot chest X-ray classification, combining meta-learning and a novel training strategy to handle real-world clinical challenges.

Contribution

The paper proposes GenCDML-FSL and GenET, novel methods for effective cross-domain, multi-label few-shot learning in chest X-ray analysis, outperforming existing approaches.

Findings

Superior performance over transfer learning methods

Effective handling of cross-domain and multi-label scenarios

Robustness in low-data settings

Abstract

Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are derived from different domains; and (iii) classes that appear during training may have partial overlap with classes of interest during evaluation. To address these challenges, we present an integrated framework called Generalized Cross-Domain Multi-Label Few-Shot Learning (GenCDML-FSL). The framework supports overlap in classes during training and evaluation, cross-domain transfer, adopts meta-learning to learn using few training samples, and assumes each chest X-ray image is either normal or associated with one or more abnormalities. Furthermore, we propose Generalized Episodic Training (GenET), a training strategy that equips models to operate with multiple challenges observed in the GenCDML-FSL scenario. Comparisons with well-established methods such as transfer learning, hybrid transfer learning, and multi-label meta-learning on multiple datasets show the superiority of our approach.