BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems
Wilfrido Gómez‐Flores,
Maria Julia Gregorio‐Calas,
Wagner Coelho de Albuquerque Pereira
Abstract:PurposeComputer‐aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI‐RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair… Show more
“…This has probably played a role in the slower development and assessment of deep learning models for these techniques. Recently released non-mammographic datasets include BrEaST [69] and BUS-BRA [70] for ultrasound and Duke Breast Cancer [71] and BreastDM [72] for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).…”
Section: Deep Learning In Breast Cancer Imaging: Datasetsmentioning
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
“…This has probably played a role in the slower development and assessment of deep learning models for these techniques. Recently released non-mammographic datasets include BrEaST [69] and BUS-BRA [70] for ultrasound and Duke Breast Cancer [71] and BreastDM [72] for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).…”
Section: Deep Learning In Breast Cancer Imaging: Datasetsmentioning
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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