The convergence of compute power and access to digital information has exponentially expanded what is possible in AI for mammography. Radiology images are very large files – a single digital mammography image can be 50MB. Training a neural network to read a mammogram, identify anomalies, and classify those anomalies correctly requires access to very large data sets and high-performance computing capabilities that were limited until recently.
Today, advances in GPU-computing coupled with expanded access to both the technology via cloud service providers and to digital radiology images, AI is finally able to begin to realize its long-promised potential.
Listen to Dr. Cody Mayo from MD Anderson Cancer Center, and Kevin Harris, CEO of CureMetrix, as they discuss how harnessing the power of AI in medical imaging is changing the landscape for how radiologists are achieving early detection of breast cancer.
By watching this webinar replay, you will learn about:
- AI-based computer aided detection (AI-CAD) that uses physics-based algorithms and deep learning;
- How AI-CAD addresses sensitivity and specificity when applied to screening mammography to improve clinical outcomes and efficiency; and
- The impressive results of a retrospective study conducted at a hospital showing a vast increase in performance between AI CAD and conventional CAD.