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
Subject
Radiology, Nuclear Medicine and imaging
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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