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Updated: Aug 23, 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

Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms.

Ioannis N Tzortzis¹, Agapi Davradou¹, Ioannis Rallis¹

¹Department of Rural Surveying Engineering and Geoinformatics Engineering, National Technical University of Athens, 157 80 Athens, Greece.

Diagnostics (Basel, Switzerland)

|October 27, 2022

Summary

This summary is machine-generated.

This study introduces an AI model for detecting digital mammogram abnormalities. It efficiently learns from limited data using tensor-based methods, achieving high accuracy and computational efficiency.

Keywords:

CP decomposition breast cancer computer-aided detection deep learning machine learning mammography screening tensor-based learning

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Limited availability of medical data and GDPR compliance necessitate data-efficient AI models.
Accurate detection of abnormalities in digital mammograms is crucial for early diagnosis.

Purpose of the Study:

To propose a tensor-based learning model for efficient abnormality detection in digital mammograms.
To develop a less data-hungry artificial intelligence framework for medical image analysis.

Main Methods:

Utilized canonical polyadic decomposition to reduce trainable parameters in a Rank-R Feedforward Neural Network (FNN) model.
Developed a tensor-based learning framework for efficient learning with small datasets.
Evaluated the model on the INBreast open-source digital mammographic database.

Main Results:

The proposed model achieved 90% ± 4% accuracy and an F1 score of 84% ± 5% on the INBreast dataset.
Demonstrated robust performance with small amounts of data compared to state-of-the-art models like AlexNet and SqueezeNet.
The framework is computationally lighter for inference due to a reduced number of trainable parameters.

Conclusions:

The tensor-based learning model offers an efficient and accurate solution for abnormality detection in digital mammograms.
The proposed artificial intelligence framework is suitable for scenarios with limited medical data.
The model provides a computationally efficient alternative for medical image analysis applications.