# Weight Group-wise Post-Training Quantization for Medical Foundation Model Source: [Weight Group-wise Post-Training Quantization for Medical Foundation Model]() Collection index: [llms.txt]() Data updated: 2026-04-11T05:00:04.811Z Authors: Yineng Chen, Peng Huang, Aozhong Zhang, Hui Guo, Penghang Yin, Shu Hu, Shao Lin, Xin Li, Tzu-Jen Kao, Balakrishnan Prabhakaran, MingChing Chang, Xin Wang Published: 2026-04-09T00:34:27.000Z ## Abstract 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-training quantization algorithm, Permutation-COMQ. It eliminates the need for backpropagation by using simple dot products and rounding operations, thereby removing hyperparameter tuning and simplifying the process. Additionally, we introduce a weight-aware strategy that reorders the weight within each layer to address the accuracy degradation induced by channel-wise scaling during quantization, while preserving channel structure. Experiments demonstrate that our method achieves the best results in 2-bit, 4-bit, and 8-bit quantization. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Medical Image Segmentation on AbdomenCT-1K (test)](): Medical Image Segmentation; AbdomenCT-1K (test); DSC: 93.615. ## Original sources and implementation [Original paper]() [PDF]()