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Related Experiment Video

Updated: Jul 29, 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

Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification.

Gonzalo Iñaki Quintana^1,2, Zhijin Li¹, Laurence Vancamberg¹

¹GE HealthCare, 283 Rue de la Minière, 78530 Buc, France.

Bioengineering (Basel, Switzerland)

|May 27, 2023

Summary

This summary is machine-generated.

This study introduces multi-scale deep learning classifiers for breast cancer detection using 2D mammograms. Combining various patch sizes and resolutions improves diagnostic accuracy, enhancing computer-aided detection (CAD) systems.

Keywords:

artificial intelligence breast imaging computer aided detection or diagnosis (CAD)convolutional neural networks (CNNs)deep learning multi-scale classification

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Published on: July 26, 2014

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Deep learning (DL) shows promise for breast cancer screening via computer-aided detection (CAD) systems.
Patch-based DL methods for 2D mammogram classification face limitations due to fixed patch sizes and unclear resolution impacts.
Optimal patch size and image resolution for detecting diverse breast cancer lesion sizes remain challenging.

Purpose of the Study:

To investigate the influence of patch size and image resolution on DL classifier performance for 2D mammograms.
To develop and evaluate novel multi-scale classifier architectures that integrate diverse patch sizes and resolutions.
To enhance the accuracy of DL-based CAD systems for breast cancer screening.

Main Methods:

Proposed multi patch-size and multi-resolution DL classifier architectures.
Implemented multi-scale classification by combining varied patch sizes and input image resolutions.
Evaluated classifier performance on the public CBIS-DDSM dataset and an internal dataset.

Main Results:

The multi-scale classifier achieved an Area Under the Curve (AUC) of 0.809 on the CBIS-DDSM dataset, a 3% improvement.
On an internal dataset, the multi-scale classifier reached an AUC of 0.722, a 5% improvement.
The proposed multi-scale approach outperformed baseline single patch size and single resolution classifiers.

Conclusions:

Multi-scale classification effectively addresses the limitations of fixed patch sizes and single resolutions in mammogram analysis.
Integrating multiple patch sizes and resolutions significantly enhances the performance of DL-based CAD systems for breast cancer screening.
The developed multi-scale architectures offer a promising advancement for more accurate and reliable mammogram interpretation.