[2604.01167] AdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2604.01167 (eess) [Submitted on 1 Apr 2026] Title:AdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation Authors:Prantik Deb, Srimanth Dhondy, N. Ramakrishna, Anu Kapoor, Raju S. Bapi, Tapabrata Chakraborti View a PDF of the paper titled AdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation, by Prantik Deb and 5 other authors View PDF HTML (experimental) Abstract: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 seg...