Quantization Techniques for Resource-Efficient Medical Image Segmentation Across Diverse Modalities

Abstract

Deep learning–based medical image segmentation models achieve high accuracy but are often impractical for clinical deployment due to their computational and memory demands. This study evaluates Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) on a U-Net architecture across seven MedSegBench datasets. Both approaches reduced model size from ~124 MB to ~31 MB (3.93× compression) and achieved 2.4–3.7× faster inference on CPU. PTQ delivered these gains with minimal accuracy loss, maintaining F1 scores nearly identical to full-precision baselines (e.g., 0.911 vs. 0.911 on Bbbc010, 0.958 vs. 0.958 on Pandental). QAT, while requiring up to 61k seconds of training, outperformed FP32 in challenging datasets, improving F1 by 25.7% on Nuclei and 3.7% on UWSkinCancer. These results demonstrate that PTQ is optimal for rapid, resource-limited deployment, whereas QAT ensures superior robustness and accuracy. Quantization thus provides a practical pathway for efficient and reliable medical image segmentation on clinical systems.