Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment
Since 2020, breast cancer has held the highest incidence rate among cancers worldwide. Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniqu...
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| Format: | Article |
| Language: | English |
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Editorial Office of Advanced Ultrasound in Diagnosis and Therapy
2025-03-01
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| Series: | Advanced Ultrasound in Diagnosis and Therapy |
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| Online Access: | https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998925986-1625668421.pdf |
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| Summary: | Since 2020, breast cancer has held the highest incidence rate among cancers worldwide. Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. This review focuses on the application of convolutional neural networks (CNNs) within DL technology in the field of breast US. It summarizes the use of DL models in breast cancer screening and in preoperative prediction of molecular subtypes, response to neoadjuvant chemotherapy (NAC), and axillary lymph node (ALN) metastasis status. The review also identifies the data limitations of using CNN models in breast US and describes the development history and current applications of DL in breast cancer screening, diagnostic guidance, and prognostic prediction. Furthermore, it discusses the future research directions and potential challenges. Advancing the development of CNN technology in breast US, and improving the generalizability and reproducibility of these models, will significantly promote their translational application in clinical settings. |
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| ISSN: | 2576-2516 |