Update README.md

README.md

@@ -11,13 +11,7 @@ AdaLoRA-QAT is an efficient, compact foundation model variant designed for accur
 
 ### 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).
+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.
 
 ### Model Sources
 
@@ -47,12 +41,6 @@ python -u inference/inference.py \
 --save_overlay ./overlay
 ```
 
-## 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 (SSIM) map indicates minor degradations primarily associated with severe motion artifacts or extreme pathologies.
-
 ## Training Details
 
 ### Training Data