Accelerating cancer diagnostics: A novel AI pipeline for efficient histopathological image analysis

Abstract

The surge in digital pathology and the vast amount of data from whole slide images (WSIs) have made it essential to develop tools that can efficiently analyze and support cancer diagnosis. An innovative artificial intelligence-based pipeline has been developed to tackle this challenge, significantly speeding up and enhancing the accuracy of breast cancer diagnosis. Created within the DigiPatICS project, in partnership with 8 hospitals across Catalonia, Spain, this pipeline begins by identifying tissue areas in WSIs and breaking them down into smaller, manageable tiles. Using smart image processing techniques, it filters out tiles that do not contain relevant information and focuses on the essential ones. Advanced deep learning algorithms then work to identify and classify different types of cells within the tissue. Proven effective on key breast cancer markers like HER2, Ki67, ER, and PR, the system precomputes the results overnight, allowing pathologists to simply load the pre-analyzed data for the areas of interest of the WSI that they select during their workday. Each WSI is analyzed in more or less 1110 s, providing reliable results that are ready when needed. Now, an integral part of routine workflows, this approach is revolutionizing how breast cancer slides are processed, enhancing diagnostic capabilities, and reshaping digital pathology.