| | --- |
| | datasets: |
| | - hongrui/mimic_chest_xray_v_1 |
| | --- |
| | # 🩺 CheXNet-MedScan-Report-Gen |
| |
|
| | **CheXNet-MedScan-Report-Gen** is an image captioning model for generating diagnostic text reports from chest X-ray images. It combines the power of a pretrained CheXNet encoder (based on DenseNet121) and a bidirectional LSTM decoder to produce sequence-based textual descriptions. |
| |
|
| | --- |
| |
|
| | ## 🧠 Model Architecture |
| |
|
| | - **Encoder:** DenseNet121 (CheXNet) with classifier removed |
| | - **Decoder:** Bidirectional LSTM with dropout |
| | - **Feature dimension:** 1024 |
| | - **Embedding dimension:** 256 |
| | - **Hidden dimension:** 512 |
| | - **Vocabulary size:** 5000 |
| | - **Dropout:** 0.5 |
| |
|
| | --- |
| |
|
| | ## 🔧 Usage |
| |
|
| | You can load the model using the Hugging Face Transformers library: |
| |
|
| | ```python |
| | from transformers import AutoModel, AutoConfig |
| | |
| | config = AutoConfig.from_pretrained("khalednabawi11/Chexnet-MedScan-Report-Gen", trust_remote_code=True) |
| | model = AutoModel.from_pretrained("khalednabawi11/Chexnet-MedScan-Report-Gen", config=config, trust_remote_code=True) |
