Adversarial Augmentation for Enhancing Classification of Mammography Images

Jendele, Lukáš; Skopek, Ondrej; Becker, Anton S.; Konukoğlu, Ender

doi:10.48550/arxiv.1902.07762

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The synthetic generated patches had clear artifacts and did not match the original dataset distribution. Jendele et al (2019) used a CycleGAN (Zhu et al, 2017) and both film scanned and digital mammograms to improve binary (malignant/benign) lesion detection using data augmentation. Detecting mammographically-occult breast cancers is another challenging topic addressed by GANs.…”

Section: Synthetic Detection Model Training Datamentioning

confidence: 99%

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Osuala

Kushibar

Garrucho

et al. 2023

Medical Image Analysis

View full text Add to dashboard Cite

Section: Synthetic Detection Model Training Datamentioning

confidence: 99%

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Osuala

Kushibar

Garrucho

et al. 2023

Medical Image Analysis

View full text Add to dashboard Cite

“…The synthetic generated patches had clear artifacts and did not match the original dataset distribution. Jendele et al [355] used a CycleGAN [54] and both film scanned and digital mammograms to improve binary (malignant/benign) lesion detection using data augmentation. Detecting mammographically-occult breast cancers is another challenging topic addressed by GANs.…”

Section: Gan Cancer Detection and Diagnosis Examplesmentioning

confidence: 99%

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

Osuala¹,

Kushibar²,

Garrucho³

et al. 2021

Preprint

View full text Add to dashboard Cite

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include high inter-observer variability, difficulty of small-sized lesion detection, nodule interpretation and malignancy determination, inter-and intra-tumour heterogeneity, class imbalance, segmentation inaccuracies, and treatment effect uncertainty. The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis. In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning. We provide a critical appraisal of the existing literature of GANs applied to cancer imagery, together with suggestions on future research directions to address these challenges. We analyse and discuss 163 papers that apply adversarial training techniques in the context of cancer imaging and elaborate their methodologies, advantages and limitations. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on GANs in the artificial intelligence community.

show abstract

“…In a similar study, Jendele et al. ( 22 ) balanced the ratio of benign and malignant lesions in the training set using a CycleGAN trained to translate healthy mammograms to mammograms containing malignant findings. The synthetic mammograms were 256×256 pixels, as higher image resolutions introduced many artifacts.…”

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

View full text Add to dashboard Cite

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.

show abstract

Adversarial Augmentation for Enhancing Classification of Mammography Images

Cited by 3 publications

References 15 publications

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

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

Contact Info

Product

Resources

About