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How AI Could Make Therapeutic Decision-making for Breast Cancer More Accurate, Affordable Published in Nature Communications, ReceptorNet is a breakthrough deep-learning algorithm that can determine hormone-receptor status - a crucial biomarker for clinicians when deciding on the appropriate treatment path for breast cancer treatment December 14, 2020 — Imagine being a doctor and having a precocious resident permanently by your side, giving you brilliant insight into disease and helping you to identify the best treatment path for your patients. A team at Salesforce Research believes this ...

 E-Mail Breast cancer affects 1/8 women in the USA, and rates of breast cancer are increasing. Humans are good at spotting cancer by looking at patterns in cells. But a new AI tool, ReceptorNet, can supplement human theragnosis by identifying the subtle differences in those patterns to inform better treatment decisions. ReceptorNet was developed in collaboration between Salesforce and Dr. David Agus at the Lawrence J. Ellison Institute for Transformative Medicine of USC. ReceptorNet could make treatment less expensive and more readily available, particularly in developing countries. Imagine ...