Update README.md

README.md

@@ -1,13 +1,57 @@
 ---
-license: apache-2.0
+license: apache-2.0          # or mit, cc-by-nc-4.0, etc. – choose appropriately
 language:
-- en
+  - en
 tags:
-- SSL
-- Asymmetry
-- Radiology
-- CT
-- Neck
-- Cancer
-- HPV
----
+  - medical-imaging
+  - self-supervised-learning
+  - mae
+  - swin-transformer
+  - 3d-vision
+  - pytorch
+  - ct
+  - opscc
+---
+
+# 3D Swin Transformer MAE for OPSCC CT Pretraining
+
+Self-supervised masked autoencoder (MAE) using a **3D Swin Transformer** backbone trained on cropped OPSCC neck CT volumes.  
+Includes asymmetry-aware loss weighting (airway + soft-tissue features) and overfitting monitoring via augmented-pair cosine similarity.
+
+## Model Details
+
+- **Architecture**: 3D Swin Transformer encoder + lightweight asymmetric decoder + auxiliary asymmetry prediction heads  
+- **Input shape**: 1×60×128×128 (single-channel CT volumes, intensities normalized to [0,1])  
+- **Pretraining objective**: Masked reconstruction (75% masking ratio) + auxiliary asymmetry regression  
+- **Drop path rate**: linear schedule up to 0.1  
+- **Training**: AdamW, lr=1e-4, batch size 2 (adjustable), early stopping + cosine sim monitoring  
+
+## Intended Use & Limitations
+
+**Primary use**: Pretraining foundation for downstream OPSCC tasks (staging, segmentation, outcome prediction)  
+**Not intended for**: Direct clinical diagnosis without fine-tuning and validation  
+
+**Limitations**:
+- Trained on limited cohort (TCIA-derived OPSCC cases)
+- Assumes cropped, skull-base-to-thoracic-inlet volumes
+- Asymmetry heuristics are rule-based → may miss subtle cases
+- No multi-modal / contrast-enhanced support yet
+
+## How to Use
+
+```bash
+# 1. Clone repo
+git clone https://huggingface.co/jdmayfield/opscc-ct-mae-swin-pretrain
+cd opscc-ct-mae-swin-pretrain
+
+# 2. Install deps
+pip install -r requirements.txt
+
+# 3. Train (or resume from checkpoint)
+python train_mae_swin3d.py \
+  --data-dir /path/to/your/cropped_volumes \
+  --output-dir ./checkpoints \
+  --epochs 100 \
+  --batch-size 2 \
+  --lr 1e-4
+