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Updated: Dec 20, 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

Mass detection in mammograms by bilateral analysis using convolution neural network.

Yanfeng Li¹, Linlin Zhang¹, Houjin Chen¹

¹School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.

Computer Methods and Programs in Biomedicine

|June 2, 2020

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This study introduces a novel bilateral Convolution Neural Network (CNN) for automatic mass detection in mammograms. The method improves diagnostic accuracy by comparing registered bilateral images, outperforming unilateral approaches.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Automatic mass detection in mammograms is critical for accurate diagnosis and assisting radiologists.
Current methods face challenges in effectively analyzing bilateral mammograms.

Purpose of the Study:

To develop a bilateral image analysis method using Convolution Neural Network (CNN) for enhanced mass detection in mammograms.
To improve the accuracy and efficiency of mass detection by leveraging bilateral image comparison.

Main Methods:

A two-network approach: a registration network using self-supervised learning for spatial transformation estimation between mammograms, and a Siamese-Faster-RCNN for mass detection.
The registration network avoids the need for point labeling by maximizing image-wise similarity.

Keywords:

Bilateral mammograms Deep learning Image registration Mass detection Self-supervised learning

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An end-to-end detection network integrating Region Proposal Network (RPN) and Siamese Fully Connected (Siamese-FC) for integrated single-image detection and bilateral comparison.

Main Results:

The method was evaluated on INbreast, BCPKUPH, and TXMD datasets.
Achieved a true positive rate (TPR) of 0.88 with 1.12 false positives per image (FPs/I) on the INbreast dataset.
Demonstrated strong performance across all tested datasets, with TPRs ranging from 0.85 to 0.88.

Conclusions:

The developed bilateral mass detection method is effective and suitable for clinical application.
The proposed method outperforms unilateral detection methods and other bilateral image fusion schemes.