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Updated: May 17, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Deep learning prediction of mammographic breast density using screening data.

Chen Chen^1,2,3, Enyu Wang^1,4, Vicky Yang Wang^1,2

¹Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Branch of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, Zhejiang, China.

Scientific Reports

|April 4, 2025

Summary

Deep learning models accurately assess mammographic breast density categories. The InceptionV3 model showed high performance, aiding radiologists in objective breast density quantification.

Keywords:

Automated breast density quantification Breast cancer risk Deep learning Mammography

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Last Updated: May 17, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats

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Published on: October 16, 2010

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Mammographic breast density assessment is crucial for breast cancer risk stratification.
Objective and consistent breast density measurement remains a challenge.

Purpose of the Study:

To evaluate deep learning models for objective assessment of four mammographic breast density categories.
To compare the performance of deep learning models against radiologists.

Main Methods:

Retrospective analysis of 57,282 mammograms from 9,621 women.
Development and evaluation of four deep learning models, including InceptionV3.
Comparison of model performance using Average Precision (AP) and against radiologist assessments.

Main Results:

The InceptionV3 model achieved high AP values across all density categories (0.857-0.953).
InceptionV3 demonstrated superior accuracy and consistency compared to radiologists, especially in denser categories.
Radiologist performance decreased significantly in heterogeneously and extremely dense categories.

Conclusions:

Deep learning models, particularly InceptionV3, offer a valuable tool for objective quantification of mammographic breast density.
AI-assisted assessment can enhance diagnostic accuracy and consistency in mammography.