ArticleIEEE access : practical innovations, open solutions2020
Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network.Journal of healthcare engineering · 2023Article
- SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation.Applied soft computing · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.SN computer science · 2022Article
- Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey.Frontiers in artificial intelligence · 2022Review
- A morphology-based radiological image segmentation approach for efficient screening of COVID-19.Biomedical signal processing and control · 2021Article
- COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.Interdisciplinary sciences, computational life sciences · 2021Review
- SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images.Expert systems with applications · 2021Article
- An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis.Entropy (Basel, Switzerland) · 2021Article
- COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images.IEEE access : practical innovations, open solutions · 2021Article
- Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this paper, we propose an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. The closest state-of-the-art gives 76.7% precision and 78.8% F1 score. The 5% improvement in precision and 3% in the F1 score demonstrate the effectiveness of the proposed method. It is worth noticing that, applying image enhancement approaches do not improve the performance of the proposed method, sometimes even harm the scores, although the enhanced images have better perceptual quality.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.