Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the ana...

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Bibliographic Details
Main Authors: Magda Marcon, Alexander Ciritsis, Cristina Rossi, Anton S. Becker, Nicole Berger, Moritz C. Wurnig, Matthias W. Wagner, Thomas Frauenfelder, Andreas Boss
Format: Article
Language:English
Published: SpringerOpen 2019-11-01
Series:European Radiology Experimental
Subjects:
Breast neoplasms
Machine learning
Ultrasonography
Online Access:http://link.springer.com/article/10.1186/s41747-019-0121-6

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http://link.springer.com/article/10.1186/s41747-019-0121-6

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