BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:PHAROS Training Series - Course 7 "Quantitative Pathologic Assessm ent using AI-based Whole-Slide Image Analysis" DTSTART;VALUE=DATE-TIME:20260320T100000Z DTEND;VALUE=DATE-TIME:20260320T120000Z DTSTAMP;VALUE=DATE-TIME:20260912T042030Z UID:indico-event-205@events.grnet.gr DESCRIPTION:\n\nPHAROS AI Factory announces the 7th Course of its Training Series\, under the title "Quantitative Pathologic Assessment using AI-bas ed Whole-Slide Image Analysis"\, under the specialisation AI4Health\, held online via Zoom.  \n\nDate: March 20th\, 2026\, at 12:00 EET \n\nLocat ion: Online via Zoom\n\nPresentation Language: Greek\n\nDescription: Dig ital pathology has revolutionized the field of histopathological analysis\ , enabling unprecedented opportunities for quantitative assessment of tiss ue specimens. This course introduces a comprehensive AI-driven infrastruct ure designed to transform conventional pathology workflows into precise\, reproducible\, and scalable analytical pipelines. We will explore cutting- edge deep learning methodologies for automated tissue segmentation\, enabl ing accurate delineation of tumor regions\, stroma\, necrosis\, and other morphologically distinct areas within whole-slide images (WSIs). Building upon this foundation\, we present advanced cell classification algorithms capable of identifying and categorizing diverse cellular populations with high accuracy and throughput. A significant focus will be placed on quanti tative feature extraction from both traditional Hematoxylin and Eosin (H&E ) stained sections and multiplex immunohistochemistry (mIHC) images. These computational approaches enable the derivation of morphometric\, spatial\ , and contextual features that capture the complex tumor microenvironment architecture. Furthermore\, we will demonstrate practical applications of deployed AI models for clinically relevant predictions\, including molecul ar mutation status inference directly from histopathology images\, patient survival outcome stratification\, and treatment response prediction throu gh pathologic complete response (pCR) assessment. These predictive models leverage the extracted quantitative features to provide actionable insight s that can guide therapeutic decision-making. Throughout the presentation\ , emphasis will be placed on infrastructure design\, model validation stra tegies\, and clinical integration considerations essential for translating AI-based pathology tools from research settings to routine diagnostic pra ctice.\n\nAudience: \n\nThis course is suitable for:\n\n\n Pathologists an d histopathology professionals\n Computational biologists and data scienti sts\n Oncology researchers and clinicians\n Medical imaging specialists\n Pharmaceutical/biotech R&D professionals\n\n\nLearning Objectives:\n\nBy p articipating in this webinar\, attendees will:\n\n\n Understand the princi ples of AI-based whole-slide image analysis and the infrastructure for dig ital pathology workflows.\n Describe deep learning approaches for automate d tissue segmentation and cell classification in histopathology.\n Identif y quantitative features extractable from H&E and multiplex immunohistochem istry images to characterize tumor microenvironment.\n Evaluate predictive AI models for mutation inference\, survival stratification\, and treatmen t response prediction.\n Recognize key considerations for validating and d eploying AI-based pathology tools into clinical practice.\n\n\nLearning Ou tcomes:\n\nAfter completing the course\, participants will be able to unde rstand:\n\n\n AI infrastructure for whole-slide image analysis\n Tissue se gmentation and cell classification AI methods\n Quantitative feature extra ction from H&E and mIHC images using AI and image analysis techniques\n Ho w predictive models work for mutations\, survival\, and pCR prediction\n A I deployment strategies in clinical pathology\n\n\nInstructor's profile:\n Georgios Manikis is a Marie Skłodowska-Curie Postdoctoral Fellow at the U niversity of Cyprus and his fellowship research is dedicated to AI-driven computational pathology and multimodal predictive modeling in oncology. He is a collaborating researcher at the Computational BioMedicine Laboratory (CBML)\, FORTH and the Karolinska Institutet\, Departent of Oncology-Path ology. His research interests lie in the areas of medical image analysis\, machine and deep learning analysis.\n\nNote: Please enter your institutio nal/corporate email when registering.\n\n \n\nhttps://events.grnet.gr/eve nt/205/ LOCATION: URL:https://events.grnet.gr/event/205/ END:VEVENT END:VCALENDAR