ArticleIEEE journal of biomedical and health informatics2020
Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank.
Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.BMC medical imaging · 2024Review
- Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study.JMIR research protocols · 2024Article
- Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study.JMIR research protocols · 2024Article
- "KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals.Scientific reports · 2024Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Review
- DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans.Expert systems with applications · 2022Article
- Artificial intelligence and IoT based prediction of Covid-19 using chest X-ray images.Smart health (Amsterdam, Netherlands) · 2022Article
- The effect of machine learning explanations on user trust for automated diagnosis of COVID-19.Computers in biology and medicine · 2022Article
- Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison.IEEE transactions on artificial intelligence · 2022Article
- NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network.International journal of intelligent systems · 2022Article
- Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data.IEEE journal of biomedical and health informatics · 2022Article
- Machine learning-based automatic detection of novel coronavirus (COVID-19) disease.Multimedia tools and applications · 2022Article
- Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.Journal of healthcare engineering · 2022Article
- Article
- COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors.IEEE journal of biomedical and health informatics · 2021Article
- Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.Journal of translational medicine · 2021Article
- A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods.Applied soft computing · 2021Article
- Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data.IEEE journal of biomedical and health informatics · 2021Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels ('difficulty' and 'diversity') are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data 'values' of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time.
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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.