AI in Breast Cancer Imaging: A Survey of Different Applications

Mendes, João; Domingues, J.M.; Aidos, Helena; Garcia, Nuno C.; Matela, Nuno

doi:10.3390/jimaging8090228

Cited by 16 publications

(14 citation statements)

References 55 publications

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“…The curvature, farthest distance, BB min distance, and BB max distance features show greater values for malignant tumor than benign. Conversely, the values of centroid distance are higher for benign as they contain compact cell arrangement [48] and lead a larger centroid distance while the scattered cell pattern and heterogeneity [49] of malignant tumors lead smaller centroid distance value. These outcomes evidenced the pattern complexity of malignant tumors that contain poorly structured and curvy lesion.…”

Section: Samentioning

confidence: 95%

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

J Digit Imaging. Inform. med.

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This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature’s significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.

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Section: Samentioning

confidence: 95%

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

J Digit Imaging. Inform. med.

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“…Data augmentation techniques aim to create high-quality datasets by generating synthetic mammograms, but further development is needed to accurately reproduce the specific characteristics of lesions. This study emphasized the need for further testing in real-world environments to ensure the safety of AI systems [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…Eric Wu et al also conducted a study that demonstrated the potential of GANs in synthesizing mammograms and detecting malignancy. These works highlighted the potential of GANs in breast cancer imaging and their use in improving the accuracy and efficiency of cancer diagnosis [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning System Technology in Identification of Women’s Breast Cancer

2023

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Background and Objectives: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. Materials and Methods: This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. Results: The major contribution of this research was the application of CNNs as a machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. Conclusions: This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.

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“…Artificial intelligence (AI) has made its way into medical diagnosis and more specifically into the field of breast cancer imaging. A review of several applications of AI to breast imaging, done by our team, can be found elsewhere [ 11 ]. Given that, the classification of DBT images into healthy/diseased classes or into benign/malignant lesions can also be done through AI.…”

Section: Introductionmentioning

confidence: 99%

Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks

2023

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Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance. To overcome this, digital breast tomosynthesis takes images (slices) at different angles that are later reconstructed into a 3D image. Having in mind that the slices are planar images where tissue overlapping does not occur, the goal of the work done here was to develop a deep learning model that could, based on the said slices, classify lesions as benign or malignant. The developed model was based on the work done by Muduli et. al, with a slight change in the fully connected layers and in the regularization done. In total, 77 DBT volumes—39 benign and 38 malignant—were available. From each volume, nine slices were taken, one where the lesion was most visible and four above/below. To increase the quantity and the variability of the data, common data augmentation techniques (rotation, translation, mirroring) were applied to the original images three times. Therefore, 2772 images were used for training. Data augmentation techniques were then applied two more times—one set used for validation and one set used for testing. Our model achieved, on the testing set, an accuracy of 93.2% while the values of sensitivity, specificity, precision, F1-score, and Cohen’s kappa were 92%, 94%, 94%, 94%, and 0.86, respectively. Given these results, the work done here suggests that the use of single-slice DBT can compare to state-of-the-art studies and gives a hint that with more data, better augmentation techniques and the use of transfer learning might overcome the use of mammograms in this type of studies.

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AI in Breast Cancer Imaging: A Survey of Different Applications

Cited by 16 publications

References 55 publications

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Application of Deep Learning System Technology in Identification of Women’s Breast Cancer

Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks

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