Create README.md

README.md

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+---
+tags:
+- lung
+- segmentation
+- medical
+- medical-imaging
+- xray
+- pytorch
+---
+# Model description
+
+This model performs automatic lung segmentation on chest X-ray images.
+It ouputs a binary lung mask that can be used as a preprocessing step before downstream tasks such as classification.
+
+This model was designed to focus the classifier on the lung region only, reducing background bias and improving interpretability.
+
+# Intended use
+
+- Automatic lung masking for chest X-ray images
+- Preprocessing step before a Covid-19 classification model
+- Research and educational purposes
+
+/!\ NOT intended for medical diagnosis or clinical use.
+
+# Model details
+- Framework : PyTorch
+- Model format : TorchScript (.pt)
+- Task : Image segmentation
+- Input : RGB chest X-ray image
+- Out put : Binary lung mask
+
+# Input format
+- Image size to 192x192 px
+- 3 channels (RGB)
+- Pixel values normalized to [0,1]
+
+# Output format
+- Single-channel binary mask
+- Vlaues in {0,1}
+- Can be resized back to original image size and applied as a mask
+
+# Example usage
+
+```python
+import torch
+
+model = torch.jit.load("mask_auto.pt", map_location="cpu")
+model.eval()
+
+with torch.no_grad():
+    mask = model(input_tensor)
+```
+
+# Training data
+
+The model was trained in chest X-ray images with corresponding lung masks, from the public dataset :
+
+**COVID-19 Radiography Dataset (Kaggle)**
+
+https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database/data
+
+The dataset contains chest X-ray images labeled as COVID, Normal, Lung-Opacity and Viral Pneumonia.
+
+# Limitations
+- Performance may degrade on low-quality or non-frontal X-ray images
+- Trained on a specific data distribution; generalization is not guaranteed
+- Should not be used for clinical decision-making
+
+# Licence
+
+This model is shared for research and educational purposes.
+
+# Author 
+
+Asma Sima