EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation

• Published in: Medical image analysis 2024-10, Vol.97, p.103277, Article 103277
• Main Authors: Zhang, Rongzhao, Chung, Albert C.S.
• Format: Article
• Language: English
• Subjects: Algorithms; Deep learning; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Model acceleration; Model compression; Neural network quantization; Neural Networks, Computer; Post-training quantization
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103277

Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-width, which plays a crucial role in the fast-growing AI industry. Despite model quantization’s success in producing well-performing low-bit models, the quantization process itself can still be expensive, which may involve a long fine-tuning stage on a large, well-annotated training set. To make the quantization process more efficient in terms of both time and data requirements, this paper proposes a fast and accurate post-training quantization method, namely EfficientQ. We develop this new method with a layer-wise optimization strategy and leverage the powerful alternating direction method of multipliers (ADMM) algorithm to ensure fast convergence. Furthermore, a weight regularization scheme is incorporated to provide more guidance for the optimization of the discrete weights, and a self-adaptive attention mechanism is proposed to combat the class imbalance problem. Extensive comparison and ablation experiments are conducted on two publicly available medical image segmentation datasets, i.e., LiTS and BraTS2020, and the results demonstrate the superiority of the proposed method over various existing post-training quantization methods in terms of both accuracy and optimization speed. Remarkably, with EfficientQ, the quantization of a practical 3D UNet only requires less than 5 min on a single GPU and one data sample. The source code is available at https://github.com/rongzhao-zhang/EfficientQ. •An efficient quantization method is designed for medical image segmentation models.•The problem is solved using ADMM, with a prior-aware and self-adaptive objective.•A UNet can be quantized to 2-bit with minimal performance degradation in 5 minutes.•Performance is verified on the public LiTS and BraTS datasets.