Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks
Paris Panagiotis Amerikanos, Ilias Maglogiannis
Abstract
Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and performance to enhance and accelerate clinical pathology procedures, such as ROI detection in WSIs. In this context, a state-of-the-art deep learning framework (Detectron2) was trained on two cases linked to the TUPAC16 dataset for object detection and on the JPATHOL dataset for instance segmentation. The predictions were evaluated against competing models and further possible improvements are discussed.
Topics & Concepts
Computer scienceArtificial intelligenceDeep learningDigital pathologySegmentationContext (archaeology)Machine learningTask (project management)Object detectionArtificial neural networkImage segmentationComputer visionPattern recognition (psychology)PaleontologyManagementEconomicsBiologyAI in cancer detectionDigital Imaging for Blood DiseasesCOVID-19 diagnosis using AI