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README.md

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-license: mit
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+license: mit
+---
+# Model Card for AdaLoRA-QAT
+
+AdaLoRA-QAT is an efficient, compact foundation model variant designed for accurate chest X-ray (CXR) lung segmentation.It adapts the Segment Anything Model (SAM) to meet strict clinical computational constraints by combining adaptive low-rank parameter fine-tuning with quantization-aware training.
+
+## Model Details
+
+### Model Description
+
+AdaLoRA-QAT introduces a two-stage fine-tuning framework for medical image segmentation. Stage 1 utilizes Adaptive Low-Rank Adaptation (AdaLoRA) to dynamically allocate rank capacity to task-relevant transformer layers in full precision. Stage 2 implements full-model quantization-aware fine-tuning (QAT) using a selective mixed-precision strategy, achieving INT8 precision for select layers while preserving fine structural fidelity. 
+
+- **Developed by:** Prantik Deb, Srimanth Dhondy, N. Ramakrishna, Anu Kapoor, Raju S. Bapi, Tapabrata Chakraborti.
+- **Funded by:** IHub-Data, International Institute of Information Technology Hyderabad. Tapabrata Chakraborti is supported by the Turing-Roche Strategic Partnership and the UCL NIHR Biomedical Research Center.
+- **Model type:** Parameter-Efficient Fine-Tuned (PEFT) Foundation Model for Chest X-ray Segmentation.
+- **License:** MIT License
+- **Finetuned from model:** Segment Anything Model (SAM).
+
+### Model Sources
+
+- **Repository:** https://prantik-pdeb.github.io/adaloraqat.github.io/ 
+- **Paper:** ADALORA-QAT: ADAPTIVE LOW RANK AND QUANTIZATION AWARE SEGMENTATION.
+
+## Uses
+
+### Direct Use
+
+* Accurate lung field segmentation for isolating pulmonary parenchyma.
+* Enhancing abnormality visibility and enabling quantitative analysis in chest radiographs.
+* Improving the reliability of computer-aided diagnosis (CAD) systems.
+* Enabling deployable foundation models on resource-constrained clinical hardware.
+
+## Bias, Risks, and Limitations
+
+* Robust generalization across deep learning models remains challenging due to anatomical variability.
+* Generalization is also challenged by pathological distortions and imaging artifacts.
+* The Structural Similarity Index (ASSIM) map indicates minor degradations primarily associated with severe motion artifacts or extreme pathologies.
+
+## Training Details
+
+### Training Data
+
+* The model was trained on 64,590 chest X-rays spanning diverse thoracic pathologies.
+* The data sources include JSRT, QaTa-COV19, COVID-19 Radiography, Chest X-Ray Pneumothorax, and COVID-QU-Ex datasets.
+
+### Training Procedure
+
+* The model utilizes a unified two-stage framework coupling adaptive low-rank encoder tuning with full model quantization-aware fine-tuning.
+* Stage 1 learns adaptive and orthogonal low-rank subspaces in full precision (FP32).
+* Stage 1 prunes redundant components to identify an efficient task-specific parameter space.
+* Stage 2 performs full-model quantization-aware fine-tuning while freezing rank masks.
+
+#### Training Hyperparameters
+
+- **Training regime:** Stage 1 uses FP32 precision. Stage 2 uses a selective mixed-precision strategy. Encoder feed-forward layers, the decoder, and the prompt encoder are quantized to INT8. Attention QKV projections and AdaLoRA parameters (P, Q, A) remain in FP32.
+- **Batch Size:** 16 during Stage 1.
+- **Learning Rates:** In Stage 1, 5e-5 for the encoder and 2e-5 for the decoder.In Stage 2, singular values are fine-tuned at 1e-6.
+
+#### Speeds, Sizes, Times
+
+* The model yields a 2.24x model compression compared to base-SAM fine-tuning.
+* Trainable parameters are reduced by 16.6x, down to 5.4M.
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+* The 64,590 CXR dataset was divided using an 80:10:10 split for experiments.
+
+#### Metrics
+
+* Dice Score (DSC).
+* Intersection over Union (IOU).
+* Normalized Surface Distance (NSD).
+* Structural Similarity Index (SSIM) to evaluate structural agreement and localized improvements.
+* Wilcoxon signed-rank test for statistical significance assessment.
+
+### Results
+
+* AdaLoRA-QAT achieves a 95.6% Dice score.
+* The model matches full-precision SAM decoder fine-tuning accuracy.
+* Statistical analysis confirms that full INT8 quantization preserves segmentation accuracy without significant degradation.
+* SSIM analysis exhibits strong structural agreement along lung boundaries and vascular regions.
+
+#### Summary
+
+AdaLoRA-QAT effectively balances accuracy, efficiency, and structural trustworthiness. It establishes a proof of concept for substantially compressing foundation models for scalable AI-assisted diagnosis without compromising diagnostic accuracy.
+
+## Model Examination
+
+* Quantization error analysis shows that FP32-INT8 quantization noise follows an approximately zero-mean Gaussian distribution.
+* There is a strong linear correlation between FP32 and INT8 weights.
+* Errors are uniformly distributed across weight magnitudes, confirming preserved numerical fidelity under low-bit quantization.
+
+### Model Architecture and Objective
+
+* The architecture is based on the Segment Anything Model (SAM) incorporating an Image Encoder (ViT), Prompt Encoder, and Mask Decoder.
+* It uses Adaptive Low-Rank Adaptation (AdaLoRA) where the vision encoder rank is reduced from 48 to 32 via importance-based pruning.
+
+#### Hardware
+
+* NVIDIA RTX A6000 GPUs (48 GB).
+
+## Model Card Authors
+
+Prantik Deb, Srimanth Dhondy, N. Ramakrishna, Anu Kapoor, Raju S. Bapi, Tapabrata Chakraborti.