Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study

Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study

Mammography interpretation is challenging with high error rates. This study aims to reduce the errors in mammography reading by mapping diagnostic errors against global mammographic characteristics using a radiomics-based machine learning approach. A total of 36 radiologists from cohort A (<i>...

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Bibliographic Details
Main Authors: Xuetong Tao, Ziba Gandomkar, Tong Li, Patrick C. Brennan, Warren Reed
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Journal of Personalized Medicine
Subjects:
mammography
mammography interpretation
diagnostic errors
radiomics
machine learning
Online Access:https://www.mdpi.com/2075-4426/13/6/888

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https://www.mdpi.com/2075-4426/13/6/888

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