Somphone Siviengphanom
Orcid: 0000-0002-2891-9217
According to our database1,
Somphone Siviengphanom
authored at least 7 papers
between 2021 and 2025.
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Bibliography
2025
A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.
J. Imaging Inform. Medicine, 2025
2023
Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.
J. Digit. Imaging, August, 2023
Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries.
Proceedings of the Medical Imaging 2023: Image Perception, 2023
Global mammographic radiomic signature can predict radiologists' difficult-to-interpret normal cases.
Proceedings of the Medical Imaging 2023: Image Perception, 2023
False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms.
Proceedings of the Medical Imaging 2023: Image Perception, 2023
2022
The reliability of radiologists' first impression interpreting a screening mammogram.
Proceedings of the Medical Imaging 2022: Image Perception, 2022
2021
An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists.
Proceedings of the Medical Imaging 2021: Image Perception, 2021