Nuclear segmentation, classification and quantification within HE stained images of colon
Objective of the challenge The objective of CONIC grand challenge was to develop algorithms that perform segmentation, classification and counting of 6 different types of nuclei within the current largest known publicly available nuclei-level dataset in computational pathology (CPath), containing around half a million labelled nuclei. This challenge consists of two separate tasks: Task […]
Digital Pathology: Transforming Pathology Education and Diagnosis
Introduction The shift from traditional pathology to digital pathology represents a significant leap in how medical professionals analyze tissue samples and educate future pathologists. Digital pathology leverages AI, computational tools, and high-resolution imaging to streamline diagnostics, improve collaboration, and enhance pathology education. This transition is empowering medical institutions and professionals with advanced tools to diagnose […]
Revolutionizing Pathology with AI and Spatial Biology
Introduction Pathology has always been at the forefront of medical diagnostics, providing crucial insights into diseases at the microscopic level. However, with the advent of spatial biology and artificial intelligence (AI), the field is undergoing a transformative shift. By integrating spatial analysis with AI-driven computational techniques, researchers and clinicians can now decode complex tissue architectures […]