Image Augmentation Techniques for Mammogram Analysis
Authors: Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A.
Journal: Journal of Imaging
Volume: 8
Issue: 5
eISSN: 2313-433X
DOI: 10.3390/jimaging8050141
Abstract:Research in the medical imaging field using deep learning approaches has become progres-sively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
https://eprints.bournemouth.ac.uk/36908/
Source: Scopus
Image Augmentation Techniques for Mammogram Analysis.
Authors: Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A.
Journal: J Imaging
Volume: 8
Issue: 5
eISSN: 2313-433X
DOI: 10.3390/jimaging8050141
Abstract:Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
https://eprints.bournemouth.ac.uk/36908/
Source: PubMed
Image Augmentation Techniques for Mammogram Analysis
Authors: Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A.
Journal: JOURNAL OF IMAGING
Volume: 8
Issue: 5
eISSN: 2313-433X
DOI: 10.3390/jimaging8050141
https://eprints.bournemouth.ac.uk/36908/
Source: Web of Science (Lite)
Image Augmentation Techniques for Mammogram Analysis
Authors: Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A.
Journal: Journal of Imaging
Abstract:Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The study aims to provide insights into augmentation and deep learning-based augmentation techniques.
https://eprints.bournemouth.ac.uk/36908/
Source: Manual
Image Augmentation Techniques for Mammogram Analysis.
Authors: Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A.
Journal: Journal of imaging
Volume: 8
Issue: 5
Pages: 141
eISSN: 2313-433X
ISSN: 2313-433X
DOI: 10.3390/jimaging8050141
Abstract:Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
https://eprints.bournemouth.ac.uk/36908/
Source: Europe PubMed Central
Image Augmentation Techniques for Mammogram Analysis
Authors: Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A.
Journal: Journal of Imaging
Volume: 8
Issue: 5
ISSN: 2313-433X
Abstract:Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The study aims to provide insights into augmentation and deep learning-based augmentation techniques.
https://eprints.bournemouth.ac.uk/36908/
Source: BURO EPrints