TY  - STD
AU  - Soumick Chatterjee
AU  - Fatima Saad
AU  - Chompunuch Sarasaen
AU  - Suhita Ghosh
AU  - Rupali Khatun
AU  - Petia Radeva
AU  - Georg Rose
AU  - Sebastian Stober
AU  - Oliver Speck
AU  - Andreas Nürnberger
PY  - 2020//
TI  - Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
N2  - CoRR abs/2006.02570The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model.
L1  - http://refbase.cvc.uab.es/files/CSS2020.pdf
N1  - MILAB
ID  - Soumick Chatterjee2020
ER  -