# Ultra-lightweight uncertainty-aware ensemble for large-scale multi-class medical MRI diagnosis

## TL;DR

This paper introduces a compact and efficient model for medical MRI diagnosis that balances accuracy and reliability, suitable for low-resource settings.

## Contribution

The novel UALE model combines lightweight micro-expert networks with uncertainty quantification for efficient and reliable medical MRI diagnosis.

## Key findings

- UALE achieves 69.1% accuracy and 68.3% F1 score with only 0.05M parameters and 0.18 GFLOPs.
- The model's uncertainty quantification enhances clinical trustworthiness and reliability.
- UALE is suitable for low-resource clinical settings due to its compactness and efficiency.

## Abstract

This paper introduces an Ultra-Lightweight Uncertainty-Aware Ensemble (UALE) model for large-scale multi-class medical MRI diagnosis, evaluated on the 2024 Benchmark Diagnostic MRI and Medical Imaging Dataset containing 40 classes and 33,616 images. The model integrates five specialized micro-expert networks, each designed to capture distinct MRI features, and combines them using a confidence-weighted ensemble mechanism enhanced with variance-based uncertainty quantification for robust, reliable predictions. With only 0.05M parameters and 0.18 GFLOPs, UALE achieves high efficiency and competitive performance among ultra-lightweight models with an accuracy of 69.1% and an F1 score of 68.3%. Besides lightweight models, the paper offers an extensive analysis and performance comparison with fifteen state-of-the-art models, discusses various datasets, elaborates on uncertainty estimates pertaining to the clinical trustworthiness of the models and possible clinical deployment, and highlights trade-offs and avenues for future work in economically constrained settings. The extreme compactness and reliability of the UALE affords it unique utility in scalable medical diagnostics suitable for low-resource clinical settings and portable imaging devices, such as rural hospitals.