Update README.md

README.md

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 ## Model description
 
-NuSPIRe (Nuclear Morphology focused Self-supervised Pretrained model for Image Representations) is a deep learning model designed to extract nuclear morphological features from DAPI-stained images. The model utilizes self-supervised pretraining, learning from 15.52 million unlabeled nuclear images from diverse tissues. NuSPIRe is optimized for biomedical image analysis tasks such as cell type identification, perturbation detection, and gene expression prediction, particularly excelling in scenarios with limited annotations.
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+NuSPIRe (Nuclear Morphology focused Self-supervised Pretrained model for Image Representations) is a deep learning framework designed to extract nuclear morphological features from DAPI-stained images for guiding field-of-view (FOV) optimization in spatial omics. Trained using self-supervised learning on 15.52 million unlabeled nuclear images from diverse tissues, NuSPIRe leverages pre-existing imaging data to identify biologically informative regions. While primarily developed for FOV selection and layout refinement, it also offers potential for broader morphology-based spatial inference.
 ![Overview Of NuSPIRe](https://huggingface.co/TongjiZhanglab/NuSPIRe/resolve/main/Images/model_overview.png)
 
 ## Training Details
@@ -133,9 +132,10 @@ print("Logits:", logits)
 print("Loss:", loss.item())
 
 ```
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 ## Citation
 
 If you use NuSPIRe in your research, please cite the following paper:
 
-Hua, Y., Li, S., & Zhang, Y. (2024). NuSPIRe: Nuclear Morphology focused Self-supervised Pretrained model for Image Representations. 
+Hua, Y., Li, S., & Zhang, Y. (2024). NuSPIRe: Nuclear Morphology focused Self-supervised Pretrained model for Image Representations. 
+-->