Breast cancer is a major global health issue in women, where diagnosis at an early stage is decisive for enhancing the effectiveness of treatment and survival. Despite the advances in imaging using medical technologies, maintaining uniformly good dia...
Deep Learning methods have become a powerful tool in medical imaging, with great potential to improve diagnostic accuracy and support early disease detection. This is especially critical for breast cancer, one of the most common cancers among women, ...
BACKGROUND: Vacuum-assisted breast biopsy (VABB) is a widely adopted minimally invasive technique for the diagnosis and treatment of breast lesions. However, the procedure heavily relies on real-time ultrasound guidance, posing significant challenges...
BACKGROUND: Precise benign and malignant breast tumors classification is essential for effective treatment planning and outcome prognostication. Medical imaging's capability to classify breast tumors has been greatly improved by the accelerated advan...
Breast cancer detection and diagnosis remain challenging due to the complexity of tumor tissues and image quality variations, which hinder early and accurate identification. Timely diagnosis is vital for initiating treatment and improving patient out...
PURPOSE: To propose a multi-parametric ultrasound imaging-based deep learning method for accurately classifying metastatic and non-metastatic axillary lymph nodes in breast cancer patients.
OBJECTIVE: This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound ima...
INTRODUCTION: Breast cancer Mammography (MAM) screening was proven to improve survival worldwide. However, younger patients with higher breast density made MAM less effective in China. It is necessary to establish Chinese-specific effective screening...
QUS spectral parametric imaging offers a fast and accurate method for breast lesion characterization. This study explored using deep CNNs to classify breast lesions from QUS spectral parametric images, aiming to enhance radiomics and conventional mac...
PURPOSE: This study aimed to develop a Deep Learning Radiomics integrated model (DLRN), which combines photoacoustic/ultrasound(PA/US)imaging with clinical and radiomics features to distinguish between luminal and non-luminal BC in a preoperative set...
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