Medical Xpress January 15, 2025
Histopathological evaluation of tumor specimens has long been essential in diagnosing breast cancer and guiding clinical decision-making. However, one of the key challenges in routine diagnostics includes the inter-observer and inter-lab variabilities present in the assessment of prognostic markers that could lead to under- and over-treatment of patients.
With the current ongoing digitization of pathology labs, it has enabled the advancement of computational pathology, which has shown the potential to improve both routine and precision diagnostics and offer decision support to both pathologists and treating physicians to improve breast cancer care.
Deep learning falls under the broader umbrella of artificial intelligence (AI) that has shown potential in advancing beyond traditional pathology by improving risk assessment, prognosis prediction, and response-to-treatment predictions....
Today's Sponsors
Today's Sponsor