Image Classification
Transformers
Safetensors
pneumonia
feature-extraction
chest_x_ray
medical_imaging
radiology
custom_code
Instructions to use ianpan/pneumonia-cxr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ianpan/pneumonia-cxr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ianpan/pneumonia-cxr", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ianpan/pneumonia-cxr", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
This model performs binary classification and segmentation for pneumonia (lung opacity) in frontal chest radiographs.
It is a tf_efficientnetv2_s backbone with a U-Net decoder and linear classification head.
The model was trained on the RSNA Pneumonia Detection Challenge dataset and the SIIM-FISABIO-RSNA COVID-19 Detection dataset.
Both of these datasets were annotated with bounding boxes, which were converted to ellipsoid segmentation masks.
Classification performance on a holdout test set of 1,334 images from the RSNA dataset and 317 images from the SIIM-FISABIO-RSNA dataset:
RSNA + SIIM-FISABIO-RSNA (n=1,651): AUC 0.900
RSNA (n=1,334): AUC 0.885
SIIM-FISABIO-RSNA (n=317) : AUC 0.914
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Model tree for ianpan/pneumonia-cxr
Base model
timm/tf_efficientnetv2_s.in21k_ft_in1k