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Updated: Mar 26, 2026

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

Multiple-Instance Learning for Anomaly Detection in Digital Mammography.

Gwenole Quellec, Mathieu Lamard, Michel Cozic

IEEE Transactions on Medical Imaging

|February 2, 2016

Summary

This summary is machine-generated.

This study introduces a computer-aided system for breast cancer detection using mammography and Multiple-Instance Learning (MIL). Weakly-supervised methods without manual segmentation surprisingly outperformed strongly-supervised ones, suggesting efficient training on large datasets.

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is the most common cancer in women.
Mammography is a key tool for breast cancer screening.
Computer-aided detection (CAD) systems can improve diagnostic accuracy.

Purpose of the Study:

To develop and evaluate a computer-aided detection and diagnosis system for breast cancer using mammography.
To explore the effectiveness of Multiple-Instance Learning (MIL) paradigms for this task.
To compare weakly-supervised versus strongly-supervised approaches for anomaly detection in mammography.

Main Methods:

Adaptive partitioning of breasts into regions.
Feature extraction including lesion detection (masses, microcalcifications) and textural features.
Classification of mammography examinations as normal or abnormal using MIL algorithms.
Evaluation of two strategies: one with manual segmentation (SVM-based) and one without (weakly-supervised MIL).

Main Results:

The weakly-supervised approach, trained simultaneously without manual segmentation, outperformed the strongly-supervised approach.
The system can highlight regions responsible for automated diagnosis of abnormal examinations.
Experiments were conducted on the publicly available DDSM dataset.

Conclusions:

Weakly-supervised anomaly detection using MIL can be highly effective for mammography-based breast cancer diagnosis.
Training anomaly detectors on large medical image archives without manual segmentation is feasible and advantageous.
This approach holds promise for improving the efficiency and scalability of breast cancer screening tools.