Three PyTorch implemented CNN architectures and their respective trainers for evaluation over the ChestX-ray14 dataset of thorax diseases. This dataset presents a class imbalanced multi-label classification problem, with 112,120 frontal-view X-rays labelled for fourteen different diseases. The architectures and trainers demonstrate use of the following techniques and optimizations:
- K-fold cross validation and early stopping with restoration of best models
- Undersampling of training set to address class imbalance problems
- Xavier initialization
- Confusion, Accuracy, Loss, Precision, and Recall recording and plotting
- Batch normalization
- Adam optimization
- ... and more! See modules for additional information.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017, July). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on (pp. 3462-3471). IEEE.