25 Enhancing Breast Cancer Screening with AI Computer Vision News such as Diffusion Weighted Imaging (DWI) to T2, eliminating relative motion between and during acquisition. Resolution can be further improved by training deep neural networks to reconstruct MRI images from suboptimal acquisitions. These methods streamline the imaging process by reducing the need for repeated acquisitions. Also, as in mammography, computer vision systems can assist radiologists by analyzing breast MRI images, detecting and segmenting suspicious lesions, and providing quantitative data for interpretation and diagnosis. Ultrasound, particularly automated breast ultrasound (ABUS), is a promising approach for examining women with dense breast tissue or abnormalities detected by other imaging modalities. In ABUS, a large transducer on the breast autonomously captures multiple images encompassing the entire breast. AI helps detect abnormalities automatically and, with robust data support, can even enable malignancy classification, significantly reducing missed detection rates. Also, ultrasound frequently serves as a tool for biopsy guidance, where AI techniques can improve the accuracy of needle guidance and tracking and reduce the duration of the procedure. Finally, standardized reporting guidelines, such as the Breast Imaging Reporting and Data System (BI-RADS), ensure consistency and accuracy in interpreting screening images across mammography, MRI, and ultrasound. Parameters such as lesion size, volume, shape, homogeneity, restriction, and other characteristics can be automatically extracted from the imaging data and presented to radiologists for informed decision-making, saving time and bolstering confidence. RSIP Vision is committed to assisting in developing AI-driven solutions that will reduce screening duration and costs while raising early detection and survival rates for breast cancer for women worldwide. Breast Mammography: suspicious lesion is circled
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