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Hermitry Ratio: Evaluating the validity of perturbation methods for explainable deep learning
Perturbation methods are model-agnostic methods used to generate heatmaps to explain black-box algorithms such as deep neural networks.
Explainable Artificial Intelligence (XAI)
Image Classification
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19
The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model.
Deep Learning for Chest X-ray Analysis: A Survey
Recent advances in deep learning have led to a promising performance in many medical image analysis tasks.
FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis
The method is tested using a held-out test dataset of 21, 176 chest x-rays (in-distribution) and a set of 14, 821 out-of-distribution x-ray images of incorrect orientation or anatomy.
