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Verified signature# Multi-Label Chest X-Ray Classifier **ResNet-50 | PyTorch | NIH ChestX-ray14 | Grad-CAM** An end-to-end medical computer vision project using an ImageNet-pretrained ResNet-50 to classify 14 thoracic conditions from chest X-ray images. The model was trained on the NIH ChestX-ray14 dataset with patient-level data splitting, class-weighted binary cross-entropy, and experiment tracking through Weights & Biases. **Held-Out Test Mean ROC-AUC: 0.823** [Live Interactive Demo](https://huggingface.co/spaces/Samer17/chest-xray-classifier-demo) | [GitHub Repository](https://github.com/Samer-4/COGS-181-DL-Final-Project) | [Training Experiment](https://wandb.ai/sam-ahmed1703-uc-san-diego/chest-xray-classification/runs/68h5gmia) ## Model Overview | Property | Description | |---|---| | Architecture | ResNet-50 with custom classification head | | Framework | PyTorch | | Initialization | ImageNet-pretrained weights | | Task | Multi-label image classification | | Dataset | NIH ChestX-ray14 | | Dat…3c996f5
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