When uncertainty guides learning: a highly effective approach to kidney disease classification in CT imaging.

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Abstract

The high cost of expert annotations significantly hinders the advancement of deep learning models for clinical medical imaging. This work introduces an efficient entropy-based active learning framework that achieves outstanding classification performance for renal abnormalities (Normal, Cyst, Stone, Tumor) in CT scans while requiring only a minimal amount of labeled data. The dataset comprises 12,446 CT slices split 70/15/15 into training (8,716), validation (1,865), and test (1,865) partitions via stratified sampling. Starting with only 200 randomly selected images and employing predictive entropy for uncertainty sampling on a pretrained ResNet-50 backbone, the proposed method attains 99.71% ± 0.25% mean test accuracy (95% CI: [99.30, 99.94]) across five independent runs after just six query cycles on the standard 12,446-image CT kidney dataset. Our method uses only 2,000 labeled training images, representing 22.9% of the 8,716-image training partition (a 77.1% reduction in required annotations relative to full supervision of the training set). This performance matches or exceeds prior fully supervised methods trained on the complete labeled training partition while demonstrating substantially improved sample efficiency, particularly in early annotation cycles where entropy-guided selection converges significantly faster than random sampling. Statistical testing across five repeated runs confirms that results are stable (Shapiro-Wilk <i>p</i> = 0.148). The framework exhibits exceptional sample efficiency as described by an empirically fitted power-law curve with a fitted exponent of 1.2, and empirically observed uncertainty decay with a rate of 0.92. These results offer both practical insights into annotation efficiency and substantial application value in the medical imaging domain.

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