metadata
library_name: transformers
tags:
- pneumonia
- chest_x_ray
- medical_imaging
- radiology
base_model:
- timm/tf_efficientnetv2_s.in21k_ft_in1k
pipeline_tag: image-classification
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