Weight Group-wise Post-Training Quantization for Medical Foundation Model

ArXi:2604.07674v1 Announce Type: new Foundation models have achieved remarkable results in medical image analysis. However, its large network architecture and high computational complexity significantly impact inference speed, limiting its application on terminal medical devices. Quantization, a technique that compresses models into low-bit versions, is a solution to this challenge. In this paper, we propose a post-