Uncertainty Quantification in COVID-19 Detection Using Evidential Deep Learning
Khosravi, B.; Faghani, S.; Ashraf Ganjouei, A.
Show abstract
Considering the immense pace of developments in deep learning (DL), its applications in medicine are relatively limited. One main issue that hinders the utilization of DL in the medical practice workflow is its reliability. A radiologist interpreting an image can easily say "I dont know", while a DL model is forced to output a result. Evidential deep learning (EDL) is one of the methods for uncertainty quantification (UQ). In this work, we aimed to use EDL to express model uncertainty in detecting COVID-19. We used SIIM-FISABIO-RSNA COVID-19 chest x-ray dataset and trained a model to diagnose typical COVID-19 pneumonia. When applied to a separate test set, it yielded an accuracy of 88% with median uncertainty scores of 0.25 and 0.07 for normal and typical COVID-19 images, respectively. Moreover, the model labeled unseen indeterminate and atypical COVID-19 x-rays with median uncertainties of 0.32 and 0.35, respectively. Our models performance was superior to the exact model trained with conventional approach of DL (i.e., using the cross-entropy loss), which is not able to express the uncertainty level. Overall, this study demonstrates applicability of UQ in disease detection that could facilitate the use of DL in practice by increasing its reliability.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SN-FPN: Self-attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation 96%
- Bayesian automatic screening of pneumoniaand lung lesions localization from CT scans. Acombined method toward a more user-centredand explainable approach 96%
- An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph 95%
Similar papers in this journal
- Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics 95%
- BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data 95%
- MultiHeadGAN: A Deep Learning Method for Low Contrast Retinal Pigment Epithelium Cells Segmentation in Fluorescent Flatmount Microscopy Images 94%
Similar papers in this journal
- ai-corona : Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans 98%
- Automated Detection of COVID-19 through Convolutional Neural Network using Chest x-ray images 97%
- Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation 97%
Similar papers in this journal
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 97%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 96%
- COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images 96%
Similar papers in this journal
- Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images 98%
- Classification of Hyper-scale Multimodal Imaging Datasets 97%
- Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.