AdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation
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 yieldin...
| Authors | Prantik Deb, Srimanth Dhondy, N. Ramakrishna et al. |
|---|---|
| Primary category | eess.IV |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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