Deep adversarial domain adaptation for breast cancer screening from mammograms

Wang, Yan; Feng, Yangqin; Zhang, Lei; Wang, Zizhou; Lv, Qing; Zhang, Yi

doi:10.1016/j.media.2021.102147

Cited by 22 publications

(14 citation statements)

References 27 publications

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“…Domain Adaptation [32] is one of the popular research directions. In recent years, many successful domain adaptation methods [33]- [37] have been widely used in medical images. Wang et al [33] propose a method called deep adversarial domain adaptation to improve the performance of breast cancer screening using mammography.…”

Section: B Domain Adaptation Methods In Medical Image Classificationmentioning

confidence: 99%

“…In recent years, many successful domain adaptation methods [33]- [37] have been widely used in medical images. Wang et al [33] propose a method called deep adversarial domain adaptation to improve the performance of breast cancer screening using mammography. They aim to extract the knowledge from a public dataset and transfer the learned knowledge to improve the detection performance on the target dataset.…”

Section: B Domain Adaptation Methods In Medical Image Classificationmentioning

confidence: 99%

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Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Jing-bo

Wang

et al. 2022

IEEE Access

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Diabetic retinopathy (DR) is an essential factor that has caused vision loss and even blindness in middle-aged and older adults. A system that can automatically perform DR diagnosis can help ophthalmologists save a lot of tedious work, such as DR grading or lesion detection. At the same time, patients can find their diseases earlier and perform the correct treatment. However, most of the existing methods require many DR annotations to train the model, and the DR data will vary to different degrees due to various shooting tools. The above problems lead to the inefficient use of existing data in the experiment, limiting actual deployment. To alleviate this problem, we propose a novel Graph Adversarial Transfer Learning (GATL) for DR diagnosis in a deep model through transfer learning, including intradomain alignment and inter-domain alignment. The proposed GATL enjoys several merits. First, our GATL adopts the self-supervised training to save the annotating cost in the target domain thus this domain adaptation method can significantly reduce annotation cost compared to the supervised approaches. Second, we introduce the graph neural network to extract potential features between unknown samples. Third, to enhance the robustness of the model, we use adversarial training to perform both inter-domain and intradomain alignment to further improve the model's classification accuracy. GATL achieved 94.3%, 97.5%, and 91.1% in accuracy, sensitivity, and specificity in the APTOS dataset and 92.7%, 95.7%, and 89.7% in the EyePACS dataset, respectively. Extensive experimental results on two challenging benchmarks, including APTOS 2019 and EyePACS, demonstrate that the proposed GATL performs favorably against baseline DR classification methods.

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Section: B Domain Adaptation Methods In Medical Image Classificationmentioning

confidence: 99%

Section: B Domain Adaptation Methods In Medical Image Classificationmentioning

confidence: 99%

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Jing-bo

Wang

et al. 2022

IEEE Access

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“… 13 Many studies are currently devoted to exploring the application of deep learning in the detection and diagnosis of related clinical diseases, including breast cancer, and further confirming the potential value of deep learning. 14 , 15 , 16 , 17 , 18 Studies have attempted to predict benign and malignant breast lesions by combining CEM images and convolutional neural networks. 19 , 20 , 21 However, small-sample and single-centre datasets cannot adequately learn diverse image features and meet the requirements for generalised performance testing.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study

Zheng¹,

Lin²,

Li³

et al. 2023

eClinicalMedicine

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“…Another imaging modality is called mammography images. Mammography is a very well and generally used approach for breast cancer screening, and it is the only image type that has been shown to decrease breast cancer mortality significantly [ 10 ]. It is an x-ray test image regarded as a reliable and accurate method for detecting breast cancer.…”

Section: Introductionmentioning

confidence: 99%

“…The critical role of a CAD system is feature extraction. Traditional feature extraction techniques have disadvantages because they lack flexibility [ 10 ]. Recently, DL approaches have been presented for breast cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

Houssein

Emam

Ali

2022

Neural Comput & Applic

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Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

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Deep adversarial domain adaptation for breast cancer screening from mammograms

Cited by 22 publications

References 27 publications

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

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