Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification
This addresses the problem of efficient medical image analysis in resource-limited environments, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of dataset compression for medical image classification by proposing the EVA coreset selection strategy, which achieves 98.27% accuracy with only 10% training data, outperforming the full training set's 97.20% and surpassing baseline methods by up to 5.61% at low selection rates.
In the medical field, managing high-dimensional massive medical imaging data and performing reliable medical analysis from it is a critical challenge, especially in resource-limited environments such as remote medical facilities and mobile devices. This necessitates effective dataset compression techniques to reduce storage, transmission, and computational cost. However, existing coreset selection methods are primarily designed for natural image datasets, and exhibit doubtful effectiveness when applied to medical image datasets due to challenges such as intra-class variation and inter-class similarity. In this paper, we propose a novel coreset selection strategy termed as Evolution-aware VAriance (EVA), which captures the evolutionary process of model training through a dual-window approach and reflects the fluctuation of sample importance more precisely through variance measurement. Extensive experiments on medical image datasets demonstrate the effectiveness of our strategy over previous SOTA methods, especially at high compression rates. EVA achieves 98.27% accuracy with only 10% training data, compared to 97.20% for the full training set. None of the compared baseline methods can exceed Random at 5% selection rate, while EVA outperforms Random by 5.61%, showcasing its potential for efficient medical image analysis.