Welcome to the repository for the CS771: Learning-based Computer Vision project, dedicated to comprehensive studies related to COVID-19 detection and classification using radiological methodologies. Check our final report 📜.
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COVID-19 Identification: Utilized CXRs to differentiate between COVID-19 and non-COVID-19 cases. Both positive and negative cases were confirmed using RT-PCR tests. We also published several papers in this field: Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence [https://pubs.rsna.org/doi/full/10.1148/radiol.2020202944] A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15,097 Patients [https://arxiv.org/abs/2210.02189]
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Pneumonia Detection: Focused on pinpointing COVID-19 induced pneumonia based on annotations from radiologists, represented as bounding boxes.
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Report Turnaround Time (RTAT) Simulation: Simulated RTAT to elucidate potential advantages in streamlining patient triage.
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Zhang R, Tie X, Qi Z, Bevins NB, Zhang C, Griner D, Song TK, Nadig JD, Schiebler ML, Garrett JW, Li K, Reeder SB, Chen GH. Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence. Radiology. 2021 Feb;298(2):E88-E97. doi: 10.1148/radiol.2020202944. Epub 2020 Sep 24. PMID: 32969761; PMCID: PMC7841876.
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Zhang R, Tie X, Garrett JW, Griner D, Qi Z, Bevins NB, Reeder SB, Chen GH. A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15,097 Patients. Arxiv 2022. ArXiv:2210.02189