让医学图像分割模型更快更省资源，实测速度大幅提升。
Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment
- 用量化感知训练优化MedSAM模型，适配医疗图像
- 推理速度显著提升，精度仍保持可用水平
- 开源代码与模型，适合部署在笔记本端使用
医学图像分割是临床实践的关键环节，当前最先进的MedSAM模型虽性能优异，但推理时消耗大量计算资源。为此，CVPR 2024举办了MedSAM on Laptop挑战赛，旨在平衡精度与处理速度。本文提出一种高效的量化感知训练流程，用于对医学图像中的Segment Anything Model进行量化，并通过OpenVINO推理引擎部署。该方法同时优化了训练时间和磁盘存储。实验表明，相比基线模型，该方法显著提升了处理速度，同时保持了可接受的精度水平。训练脚本、推理脚本及量化模型已公开于https://github.com/AVC2-UESTC/QMedSAM。
原文摘要 · Abstract (English)
Medical image segmentation is a critical component of clinical practice, and the state-of-the-art MedSAM model has significantly advanced this field. Nevertheless, critiques highlight that MedSAM demands substantial computational resources during inference. To address this issue, the CVPR 2024 MedSAM on Laptop Challenge was established to find an optimal balance between accuracy and processing speed. In this paper, we introduce a quantization-aware training pipeline designed to efficiently quantize the Segment Anything Model for medical images and deploy it using the OpenVINO inference engine. This pipeline optimizes both training time and disk storage. Our experimental results confirm that this approach considerably enhances processing speed over the baseline, while still achieving an acceptable accuracy level. The training script, inference script, and quantized model are publicly accessible at https://github.com/AVC2-UESTC/QMedSAM.
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