Performing a Research Study Using Open-Source Deep Learning Models
Author:
Affiliation:
1. Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Funder
National Research Foundation of Korea
Publisher
XMLink
Link
https://kjronline.org/pdf/10.3348/kjr.2023.0869
Reference12 articles.
1. Uphold the code: how complete, detailed and open code can enhance understanding, improve reproducibility, and change the shape of the research article. accessed on September 6, 2023. Available at: https://theplosblog.plos.org/2022/05/uphold-the-code/
2. Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial
3. Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs
4. Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort
5. Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model
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