Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

PurposeTo implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.Materials and MethodsA total of 298 patients were identified from a retrospective r...

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Bibliographic Details
Main Authors: Xiao Chen, Yang Zhang, Jiahuan Zhou, Xiao Wang, Xinmiao Liu, Ke Nie, Xiaomin Lin, Wenwen He, Min-Ying Su, Guoquan Cao, Meihao Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Oncology
Subjects:
architectural distortion
breast cancer diagnosis
deep learning
digital breast tomosynthesis
radiomics
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.991892/full

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https://www.frontiersin.org/articles/10.3389/fonc.2022.991892/full

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