MedPTQ: a practical pipeline for real post-training quantization in 3D medical image segmentation

We have introduced MedPTQ, a real post-training quantization pipeline that delivers INT8 inference for SOTA 3D artificial intelligence (AI) models in medical imaging segmentation. MedPTQ effectively reduces real-world model size, computational requirements, and inference latency without compromising segmentation accuracy on modern GPUs, as evidenced by mDSC comparable to full-precision baselines. We validate MedPTQ across a diverse set of AI architectures, ranging from convolutional-neural-network-based to transformer-based models, and a wide variety of medical imaging datasets. These datasets are collected from multiple hospitals with distinct imaging protocols, cover different body regions (such as the brain, abdomen, or full body), and include multiple imaging modalities [computed tomography (CT) and magnetic resonance imaging (MRI)]. Collectively, these results highlight our MedPTQ's strong generalizability and adaptability for a broad spectrum of medical imaging tasks.