Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is cha...

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Bibliographic Details
Main Authors: Chiharu Kai, Sachi Ishizuka, Tsunehiro Otsuka, Miyako Nara, Satoshi Kondo, Hitoshi Futamura, Naoki Kodama, Satoshi Kasai
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Cancers
Subjects:
mammogram
breast composition
mammary gland content ratio
regression deep convolutional neural network
explainable AI
Online Access:https://www.mdpi.com/2072-6694/15/10/2794

Internet

https://www.mdpi.com/2072-6694/15/10/2794

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