Chest X-ray (CXR) segmentation is an important step in computer-aided diagnosis, yet deploying large foundation models in clinical settings remains challenging due to computational constraints. We propose AdaLoRA-QAT, a two-stage fine-tuning framework that combines adaptive low-rank encoder adaptation with full quantization-aware training. Adaptive rank allocation improves parameter efficiency, while selective mixed-precision INT8 quantization preserves structural fidelity crucial for clinical reliability. Evaluated across large-scale CXR datasets, AdaLoRA-QAT achieves 95.6% Dice, matching full-precision SAM decoder fine-tuning while reducing trainable parameters by 16.6\times and yielding 2.24\times model compression. A Wilcoxon signed-rank test confirms that quantization does not significantly degrade segmentation accuracy. These results demonstrate that AdaLoRA-QAT effectively balances accuracy, efficiency, and structural trust-worthiness, enabling compact and deployable foundation models for medical image segmentation. Code and pretrained models are available at: https://prantik-pdeb.github.io/adaloraqat.github.io/

翻译：胸部X光（CXR）分割是计算机辅助诊断中的重要步骤，然而，由于计算约束，在临床环境中部署大型基础模型仍面临挑战。我们提出AdaLoRA-QAT，这是一个两阶段微调框架，它将自适应低秩编码器适配与全量化感知训练相结合。自适应秩分配提高了参数效率，而选择性混合精度INT8量化则保留了临床可靠性所需的结构保真度。在大规模CXR数据集上的评估显示，AdaLoRA-QAT实现了95.6%的Dice系数，与全精度SAM解码器微调性能相当，同时将可训练参数量减少16.6倍，并实现2.24倍的模型压缩。Wilcoxon符号秩检验证实，量化并未显著降低分割精度。这些结果表明，AdaLoRA-QAT有效平衡了准确性、效率和结构可信度，为医学图像分割提供了紧凑且可部署的基础模型。代码与预训练模型可访问：https://prantik-pdeb.github.io/adaloraqat.github.io/