Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images

Swiecicki, Albert; Buda, Mateusz; Saha, Ashirbani; Li, Nianyi; Ghate, Sujata V.; Walsh, Ruth; Mazurowski, Maciej A.

doi:10.1117/12.2551379

Cited by 2 publications

(1 citation statement)

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“…Fan et al [ 97 ] planned to produce super-resolution apparent-diffusion-coefficient (SR-ADC) images with an SR generative adversarial (SRGAN) and an enhanced deep SR (EDSR) network, as well as bicubic interpolation. Another work by Swiecicki et al [ 98 ] used digital breast tomosynthesis data for detecting anomalies by using GAN to complete an image. The detection system reported in this study yielded promising findings, as it was able to identify suspicious spots without the need for training images with abnormalities.…”

Section: Breast-cancer-diagnosis Methods Based On Deep Learningmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

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Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

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Section: Breast-cancer-diagnosis Methods Based On Deep Learningmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

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Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Nasser

Yusof

2023

Diagnostics

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Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients’ survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

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Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images

Cited by 2 publications

References 0 publications

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

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