Update README.md

README.md

@@ -1,13 +1,22 @@
 ---
 library_name: transformers
-tags: []
+base_model:
+- facebook/sam2-hiera-tiny
+pipeline_tag: image-segmentation
 ---
+library_name: transformers
+tags:
+- medical-imaging
+- image-segmentation
+- ultrasound
+- foundation-models
+- sam
 
-# Model Card for Model ID
+# Model Card for Sam2Rad
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+Sam2Rad is a prompt-learning framework that adapts Segment Anything Model (SAM/SAM2) for autonomous segmentation of bony structures in ultrasound images. It eliminates the need for manual prompts through a lightweight Prompt Predictor Network (PPN) that generates learnable prompts directly from image features. Compatible with all SAM variants, it supports three modes: autonomous operation, semi-autonomous human-in-the-loop refinement, and fully manual prompting.
 
 ## Model Details
 
@@ -15,22 +24,20 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Assefa Seyoum Wahd, Banafshe Felfeliyan, Yuyue Zhou, et al. (University of Alberta and McGill University)
+- **Funded by [optional]:** TD Ready Grant, IC-IMPACTS, One Child Every Child, Arthritis Society, Alberta Innovates AICE Concepts
+- **Shared by:** Ayyuce Demirbas
+- **Model type:** Vision Transformer (ViT)-based segmentation model with prompt learning
+- **Language(s) (NLP):** N/A (Image-based model)
+- **License:** [More Information Needed] (Check GitHub for exact license)
+- **Finetuned from model [optional]:** SAM/SAM2 (Hiera-Tiny, Hiera-Small, Hiera-Large, Hiera-Base+)
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Repository:** [GitHub](https://github.com/aswahd/SamRadiology)
+- **Paper:** "Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts"
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
@@ -41,159 +48,44 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+- Automatic segmentation of bones in musculoskeletal ultrasound images (hip, wrist, shoulder)
+- Integration into clinical workflows for real-time analysis or data labeling
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+- Active learning frameworks requiring rapid annotation
+- Multi-class medical image segmentation with task-specific adaptations
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+- Non-ultrasound modalities (e.g., MRI, CT) without retraining
+- Images with severe artifacts or non-anatomical structures
+- Non-medical image segmentation
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+- **Domain specificity:** Trained on musculoskeletal ultrasound; performance degrades on unseen modalities.
+- **Anatomical limitations:** May struggle with atypical anatomies or surgical implants.
+- **Ethical considerations:** Not validated for diagnostic use without clinician oversight.
 
 ### Recommendations
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Validate outputs against expert annotations in clinical deployments. Retrain PPN when applying to new anatomical regions or imaging protocols.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
-
-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+```python
+# see GitHub for implementation https://github.com/aswahd/SamRadiology
 
-[More Information Needed]
+from transformers import AutoModel
+model = AutoModel.from_pretrained("ayyuce/sam2rad")