clara_xray_classification_chest_no_amp is a pre-trained densenet121 model for disease pattern detection in chest x-rays.
Description
clara_xray_classification_chest_no_amp is a pre-trained densenet121 model for disease pattern detection in chest x-rays.
Model Overview
The model is trained using a densenet121 model [1] for disease pattern detection in chest x-rays [2].
Data
This model is trained using PLCO training data and evaluated on the PLCO validation data.
You can apply for access to the dataset at: https://biometry.nci.nih.gov/cdas/learn/plco/images/
Training configuration
The provided training configuration required 12GB-memory GPUs. The training was performed with command train.sh, which required 12GB-memory GPUs.
Training Graph Input Shape: 256 x 256
Input and output formats
Input: 16-bit CXR png
Output: 15 binary labels, each bit is corresponding to the prediction of 'Nodule', 'Mass', 'Distortion of Pulmonary Architecture', 'Pleural Based Mass', 'Granuloma', 'Fluid in Pleural Space', 'Right Hilar Abnormality', 'Left Hilar Abnormality', 'Major Atelectasis', 'Infiltrate', 'Scarring', 'Pleural Fibrosis', 'Bone/Soft Tissue Lesion', 'Cardiac Abnormality', 'COPD'
Scores
This model achieves the following Dice score on the validation data
- Averaged AUC over all disease categories: 0.797
Availability
In order to access this model please apply for access
https://developer.nvidia.com/clara
This model is usable only as part of Transfer Learning & Annotation Tools in Clara Train SDK container. You can download the model from NGC registry as described in Getting Started Guide.
Compatibility
This model is only compatible with Clara Train SDK v2.0 and will not work with v1.1 and v1.0.
Disclaimer
This is an example, not to be used for diagnostic purposes
License
End User License Agreement is included with the product. Licenses are also available along with the model application zip file. By pulling and using the Clara Train SDK container and downloading models, you accept the terms and conditions of these licenses.
References
[1] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. https://arxiv.org/abs/1608.06993.
[2] Wang, Xiaosong, et al. "Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. https://arxiv.org/abs/1705.02315.