Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques
for Enhanced Data Analysis
- URL: http://arxiv.org/abs/2303.01178v1
- Date: Thu, 2 Mar 2023 11:47:55 GMT
- Title: Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques
for Enhanced Data Analysis
- Authors: Manuel Cossio
- Abstract summary: Data augmentation has emerged as a promising and cost-effective technique for increasing the size and diversity of the training dataset.
We conducted an in-depth study of all data augmentation techniques used in medical imaging, identifying 11 different purposes and collecting 65 distinct techniques.
It is expected that the list of available techniques will expand in the future, providing researchers with additional options to consider.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In the realm of medical imaging, the training of machine learning models
necessitates a large and varied training dataset to ensure robustness and
interoperability. However, acquiring such diverse and heterogeneous data can be
difficult due to the need for expert labeling of each image and privacy
concerns associated with medical data. To circumvent these challenges, data
augmentation has emerged as a promising and cost-effective technique for
increasing the size and diversity of the training dataset. In this study, we
provide a comprehensive review of the specific data augmentation techniques
employed in medical imaging and explore their benefits. We conducted an
in-depth study of all data augmentation techniques used in medical imaging,
identifying 11 different purposes and collecting 65 distinct techniques. The
techniques were operationalized into spatial transformation-based, color and
contrast adjustment-based, noise-based, deformation-based, data mixing-based,
filters and mask-based, division-based, multi-scale and multi-view-based, and
meta-learning-based categories. We observed that some techniques require manual
specification of all parameters, while others rely on automation to adjust the
type and magnitude of augmentation based on task requirements. The utilization
of these techniques enables the development of more robust models that can be
applied in domains with limited or challenging data availability. It is
expected that the list of available techniques will expand in the future,
providing researchers with additional options to consider.
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