Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies
@article{Hryniewska2021ChecklistFR, title={Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies}, author={Weronika Hryniewska and Przemyslaw Bombinski and Patryk Szatkowski and Paulina Tomaszewska and Artur Przelaskowski and Przemysław Biecek}, journal={Pattern Recognition}, year={2021}, volume={118}, pages={108035 - 108035} }
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References
SHOWING 1-10 OF 91 REFERENCES
EXPLAINABLE-BY-DESIGN APPROACH FOR COVID-19 CLASSIFICATION VIA CT-SCAN
- MedicinemedRxiv
- 2020
An eXplainable Deep Learning approach to detect COVID-19 from computer tomography (CT) - Scan images is proposed and produces highly interpretable results which may be helpful for the early detection of the disease by specialists.
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
- Computer ScienceIEEE Transactions on Medical Imaging
- 2020
Experimental results show that the proposed patch-based convolutional neural network approach achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
- MedicineArXiv
- 2020
Five different deep learning models and their Ensemble have been used, to classify COVID-19, pneumoniae and healthy subjects using Chest X-Ray, and qualitative results depicted the ResNets to be the most interpretable model.
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning
- Computer Science, MedicineIEEE Transactions on Medical Imaging
- 2020
This paper proposes an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances, which can semantically generate deep3D instances following the possible infection area.
COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images
- Computer Science
- 2020
A deep learning-based approach using Densenet-121 to effectively detect COVID-19 patients and a website that takes chest radiology images as input and generates probabilities of the presence of CO VID-19 or pneumonia and a heatmap highlighting the probable infected regions is developed.
Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays
- Computer Science, MedicineExperimental and therapeutic medicine
- 2020
This study presents an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically-relevant image regions, achieving an overall area under the curve of 1 for a binary classification problem across a 5-fold training/testing dataset.
Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
- Computer ScienceArXiv
- 2020
This paper investigates how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and shows that the uncertainty in prediction is highly correlates with accuracy of prediction.
Automated detection of COVID-19 cases using deep neural networks with X-ray images
- MedicineComputers in Biology and Medicine
- 2020
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images
- Computer Science2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- 2020
An explainable deep neural networks (DNN)-based method for automatic detection of COVID-19 symptoms from chest radiography (CXR) images, which is called ‘DeepCOVIDExplainer’ and provides human-interpretable explanations for the diagnosis.
Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs
- MedicineArXiv
- 2020
This study trains and evaluates three model architectures, proposed for chest radiograph analysis, under varying conditions, and finds issues that discount the impressive model performances proposed by contemporary studies on this subject, and proposes methodologies to train models that yield more reliable results.