Benford’s Law-Guided Quantization: Log-Uniform Weight Compression for Efficient Medical Vision Models

• Arthur Negrão UFOP
• Guilherme Silva UFOP
• Matheus Vieira UFOP
• Ederson N. F. G. Júnior UFOP
• Eduardo José da Silva Luz UFOP
• Pedro Silva UFOP

DOI: https://doi.org/10.5753/sbcas.2026.21342

Abstract

Benford-Quant, a post-training quantization method guided by Benford’s Law, is evaluated for medical image classification. The study considers ResNet-18, ResNet-50, and ViT-Base on the BloodMNIST, PathMNIST, and OrganCMNIST datasets, with comparisons against RTN and NF4. Results show that BenQ preserves competitive F1-score while achieving up to 7.7× memory reduction, indicating that its benefits extend beyond LLMs. This behavior persists even under challenging conditions such as Gaussian noise and contrast variations, where non-uniform quantization methods tend to exhibit greater robustness. These findings position BenQ as a viable alternative for medical vision models in resource-constrained environments.

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