Breast Cancer Detection Using GAN for Limited Labeled Dataset

Desai, Shrinivas D.; Chickerur, Satyadhyan; Verma, Nitin; Gupta, Puneet; Ramya, S. P.

doi:10.1109/cicn49253.2020.9242551

Cited by 26 publications

(13 citation statements)

References 11 publications

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“…AI has already been applied to breast cancer based on different classes of data, to inform diagnosis, treatment planning and prognosis 48,49 . For example, pattern recognition and data augmentation proved to be promising approaches to assist in generating accurate diagnoses from mammography images 50,51 . Transcriptome data were also employed to develop ML-based analysis pipelines for breast cancer subtyping, diagnosis, patient stratification and identification of altered pathways 52 , and these techniques may improve the accuracy of cancer prognosis in the future.…”

Section: Discussionmentioning

confidence: 99%

Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer

Guttà

Morhard²,

Rehm

2022

Preprint

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Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, GAN-based data augmentation therefore allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

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Section: Discussionmentioning

confidence: 99%

Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer

Guttà

Morhard²,

Rehm

2022

Preprint

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“…Other studies used the synthetic mammograms as data augmentation to improve the performance in different downstream tasks. Synthetic data augmentation using GANs was evaluated in breast cancer classification by Shrinivas et al (21). The proposed model, a Deep Convolutional GAN (DCGAN), synthesized FFDM with 256×256 image resolution.…”

Section: A B Cmentioning

confidence: 99%

“…Synthetic data augmentation using GANs was evaluated in breast cancer classification by Shrinivas et al. ( 21 ). The proposed model, a Deep Convolutional GAN (DCGAN), synthesized FFDM with 256×256 image resolution.…”

Section: Introductionmentioning

confidence: 99%

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

et al. 2023

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Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in high-resolution mammograms. The training images were split by breast density BI-RADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.

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“…In addition, several DL-based augmentation systems employ adversarial training (including GAN-based and other adversarial learning networks) [ 50 , 51 ]. GANs are a widely used data augmentation approach to detect patterns and variances in image samples from the training dataset [ 52 , 53 ]. They have also been used for breast mass detection [ 53 ], mass classification [ 10 ] as well as mass segmentation [ 54 ].…”

Section: Advanced Augmentation Techniquesmentioning

confidence: 99%

“…GANs are a widely used data augmentation approach to detect patterns and variances in image samples from the training dataset [ 52 , 53 ]. They have also been used for breast mass detection [ 53 ], mass classification [ 10 ] as well as mass segmentation [ 54 ]. The realistic level of artificially generated images for medical scenarios is still a debate matter [ 5 ].…”

Section: Advanced Augmentation Techniquesmentioning

confidence: 99%

Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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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.

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Breast Cancer Detection Using GAN for Limited Labeled Dataset

Cited by 26 publications

References 11 publications

Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer

Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

Image Augmentation Techniques for Mammogram Analysis

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