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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

Image Augmentation Techniques for Mammogram Analysis.

Parita Oza¹, Paawan Sharma¹, Samir Patel¹

¹Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India.

Journal of Imaging

|May 27, 2022

Summary

This summary is machine-generated.

Data augmentation techniques enhance deep learning models for medical imaging by increasing training data size. This survey explores methods to improve mammogram analysis and combat model overfitting.

Keywords:

data augmentation deep learning mammograms medical imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning in medical imaging requires large, annotated datasets.
Manual annotation by radiologists is time-consuming and limits dataset size.
Small datasets lead to overfitting and poor generalization in deep learning models.

Purpose of the Study:

To survey data augmentation techniques for mammogram images.
To provide insights into basic and deep learning-based augmentation methods.
To address challenges of small dataset sizes in medical image analysis.

Main Methods:

Review of various data augmentation techniques.
Exploration of traditional image transformations.
Analysis of deep learning-based augmentation strategies.

Main Results:

Data augmentation effectively increases training dataset size.
Augmentation improves the performance of deep learning models on unseen data.
Various techniques offer solutions to limited mammogram dataset availability.

Conclusions:

Data augmentation is crucial for developing robust deep learning models in medical imaging.
Both basic and advanced augmentation methods can mitigate overfitting.
Further research into tailored augmentation for mammography is warranted.