Update README.md

README.md

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 license: apache-2.0
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-# Model Card for ToothSeg
+# Model Card for OralSeg
 
 <!-- Provide a quick summary of what the model is/does. -->
-ToothSeg focuses on tooth and bone object detection and segmentation in dental CBCT imaging, and is applicable to surgical planning, orthodontic design, implant planning, and academic research.
+OralSeg focuses on tooth and bone object detection and segmentation in dental CBCT imaging, and is applicable to surgical planning, orthodontic design, implant planning, and academic research.
 
 
 ## Model Details
@@ -13,13 +13,13 @@ ToothSeg focuses on tooth and bone object detection and segmentation in dental C
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
-ToothSeg is an advanced instance segmentation model based on dental CBCT, developed in our study, designed to accurately segment the maxilla, mandible, 32 teeth, and bilateral mandibular canals in large-scale dental CBCT images. The model is trained using high-precision annotations from expert dental professionals and is characterized by its efficiency, robustness, and accuracy, making it well-suited for clinical research and practical applications.
+OralSeg is an advanced instance segmentation model based on dental CBCT, developed in our study, designed to accurately segment the maxilla, mandible, 32 teeth, and bilateral mandibular canals in large-scale dental CBCT images. The model is trained using high-precision annotations from expert dental professionals and is characterized by its efficiency, robustness, and accuracy, making it well-suited for clinical research and practical applications.
 
 - **Developed by:** AIADIR
 - **Funded by:** The University of Hong Kong, Faculty of Dentistry
 - **Model type:** Dental CBCT image instance segmentation model
 - **Language:** English
-- **License:** ToothSeg © 2025 by AIADIR is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The model is released for non-commercial use. 
+- **License:** OralSeg © 2025 by AIADIR is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The model is released for non-commercial use. 
 
 ## Training Details
 
@@ -27,7 +27,7 @@ ToothSeg is an advanced instance segmentation model based on dental CBCT, develo
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-ToothSeg was trained and tested using 100 high-quality, high-resolution dental CBCT images. All images were manually annotated at pixel-level precision by expert dental professionals, covering:
+OralSeg was trained and tested using 100 high-quality, high-resolution dental CBCT images. All images were manually annotated at pixel-level precision by expert dental professionals, covering:
 
 - Maxilla
 - Mandible
@@ -39,7 +39,7 @@ These detailed annotations provide the network with accurate and rich target con
 
 ## Model Architecture
 
-ToothSeg model is based on the SwinUnetR architecture and incorporates our proprietary data augmentation strategies and training schemes to further enhance its performance in dental instance segmentation tasks. This architecture features the following key components:
+OralSeg model is based on the SwinUnetR architecture and incorporates our proprietary data augmentation strategies and training schemes to further enhance its performance in dental instance segmentation tasks. This architecture features the following key components:
 
 - **Multi-scale feature extraction:** Uses a Swin Transformer-based feature extraction module to process anatomical structures of various sizes in CBCT images.
 - **UNet-style convolutional structure:** Features extracted by the encoder are progressively upsampled and fused via a symmetric decoder path, preserving spatial information and improving segmentation accuracy.