Somphone Siviengphanom

According to our database, Somphone Siviengphanom authored at least 6 papers between 2021 and 2023.

Collaborative distances:

• Dijkstra number of six.
• Erdős number of four.

2023

Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.

Somphone Siviengphanom, Ziba Gandomkar, Sarah J. Lewis, Patrick C. Brennan

J. Digit. Imaging, August, 2023

Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries.

Xuetong Tao, Somphone Siviengphanom, Ziba Gandomkar, Tong Li, Warren M. Reed, Patrick C. Brennan

Proceedings of the Medical Imaging 2023: Image Perception, 2023

Global mammographic radiomic signature can predict radiologists' difficult-to-interpret normal cases.

Somphone Siviengphanom, Ziba Gandomkar, Sarah J. Lewis, Patrick C. Brennan

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.

Ziba Gandomkar, Sarah J. Lewis, Somphone Siviengphanom, Dennis Wong, Ernest U. Ekpo, Moayyad E. Suleiman, Xuetong Tao, Warren M. Reed, Patrick C. Brennan

Proceedings of the Medical Imaging 2023: Image Perception, 2023

2022

The reliability of radiologists' first impression interpreting a screening mammogram.

Ziba Gandomkar, Somphone Siviengphanom, Moayyad E. Suleiman, Dennis Wong, Warren M. Reed, Ernest U. Ekpo, Dong Xu, Sarah J. Lewis, Patrick C. Brennan

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.

Ziba Gandomkar, Ernest U. Ekpo, Sarah J. Lewis, Moayyad E. Suleiman, Somphone Siviengphanom, Tong Li, Patrick C. Brennan

Proceedings of the Medical Imaging 2021: Image Perception, 2021