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Related Experiment Video

Updated: Jul 4, 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 for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.

Vineela Nalla¹, Seyedamin Pouriyeh¹, Reza M Parizi²

¹Department of Information Technology, Kennesaw State University, Kennesaw, Georgia, USA.

Current Problems in Diagnostic Radiology

|February 1, 2024

Summary

This summary is machine-generated.

This study reviews public mammography datasets crucial for training deep learning (DL) models in breast cancer detection. It provides a resource to improve autonomous diagnosis and model comparison.

Keywords:

Breast Cancer Cancer screening Deep Learning FFDM (Full Field Digital Mammogram)Mammography Public datasets

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Last Updated: Jul 4, 2025

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

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of cancer in women, with early detection via mammography improving survival rates.
Deep learning (DL) models require diverse datasets for accurate autonomous breast cancer diagnosis.
Limited availability of public mammography datasets hinders DL model development and comparative analysis.

Purpose of the Study:

To comprehensively describe and review currently available public mammography datasets.
To provide a valuable resource for researchers and practitioners in breast cancer DL.
To facilitate the development and assessment of more effective DL models for breast cancer detection.

Main Methods:

Systematic review and description of publicly accessible mammography datasets.
Analysis of dataset characteristics and usability for DL model training.
Summarization of information on existing public mammography datasets.

Main Results:

A curated selection of public mammography datasets is described.
The usability of these datasets for DL applications is reviewed.
Identified challenges in dataset availability and comparability for DL models.

Conclusions:

Public mammography datasets are essential for advancing DL in breast cancer diagnosis.
This work bridges a knowledge gap by consolidating information on available datasets.
Improved understanding and utilization of these datasets will enhance DL model development and validation.