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A Comparative Evaluation of Meta-Learning Models for Few-Shot Chest X-Ray Disease Classification.

Luis-Carlos Quiñonez-Baca¹, Graciela Ramirez-Alonso¹, Fernando Gaxiola²

¹Computer Vision and Data Science Lab, Facultad de Ingeniería, Universidad Autónoma de Chihuahua, Circuito Universitario Campus II, Chihuahua 31125, Mexico.

Diagnostics (Basel, Switzerland)

|September 27, 2025

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Meta-learning effectively classifies thoracic diseases from limited chest X-ray data. Prototype-based approaches, like Prototypical Networks with DenseNet-121, offer robust and efficient few-shot learning for medical imaging diagnostics.

Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Limited labeled data hinders deep learning for medical diagnostics, especially for rare diseases.
Deep learning models require large annotated datasets, which are often unavailable in healthcare.
Meta-learning enables rapid adaptation to new tasks with minimal labeled data.

Purpose of the Study:

To evaluate meta-learning models for thoracic disease classification using chest X-rays.
To compare the performance of Prototypical Networks, Relation Networks, MAML, and FoMAML.
To identify optimal backbone architectures for meta-learning in this context.

Main Methods:

Comparative evaluation of four meta-learning algorithms: Prototypical Networks, Relation Networks, MAML, and FoMAML.

Keywords:

chest X-ray disease classification few-shot learning meta-learning

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Assessment of five backbone architectures (ConvNeXt, DenseNet-121, ResNet-50, MobileNetV2, ViT) with Prototypical Networks.

Experiments conducted on the ChestX-ray14 dataset using a 2-way, k-shot setting.

Main Results:

Prototypical Networks with DenseNet-121 yielded the best performance (Recall: 68.1%, F1: 67.4%, Precision: 0.693) in the 2-way, 10-shot configuration.
Hernia classification achieved the highest accuracy in disease-specific analysis.
Prototypical and Relation Networks showed superior computational efficiency (fewer FLOPs, shorter execution times) compared to MAML and FoMAML.

Conclusions:

Prototype-based meta-learning, especially with DenseNet-121, is a robust and efficient method for few-shot chest X-ray classification.
This approach shows significant potential for clinical applications with scarce annotated medical data.
Meta-learning offers a viable solution to overcome data limitations in medical AI development.