Aligned with
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.
This track focuses on the integration of artificial intelligence and deep learning techniques in the field of computational pathology. Discussions will encompass innovative algorithms and their applications in enhancing diagnostic accuracy and efficiency.
This session will explore methodologies for stain normalization and standardization in histopathological images. Emphasis will be placed on improving reproducibility and comparability of results across different laboratories.
This track will address advanced methods for the detection and classification of various tissue structures using computational techniques. Participants will present novel approaches that enhance the precision of tissue analysis.
This session will delve into the detection and discovery of predictive and prognostic tissue biomarkers through computational pathology. The focus will be on methodologies that facilitate the identification of biomarkers linked to patient outcomes.
This track will cover the latest advancements in whole-slide image analysis, including segmentation and feature extraction techniques. The session aims to highlight the role of computational tools in transforming large-scale histopathological data into actionable insights.
This session will focus on the methodologies for the registration of whole-slide images, addressing challenges and solutions in aligning images from different sources. Participants will share innovative approaches to improve the accuracy of image registration.
This track will explore advancements in immunohistochemistry scoring methodologies, emphasizing automated and semi-automated approaches. The goal is to enhance the reliability and reproducibility of scoring in clinical settings.
This session will discuss the latest developments in multiplexed staining techniques for tissue analysis. The focus will be on how these techniques can provide comprehensive insights into tissue microenvironments.
This track will investigate the applications of unlabeled multiplexing in computational pathology. Discussions will center on innovative strategies that leverage unlabeled data for enhanced tissue characterization.
This session will focus on the role of crowdsourcing in the collection of ground truth data for machine learning applications in pathology. Participants will share successful case studies and methodologies that harness community engagement.
This track will explore the practical applications of computational pathology in clinical settings. Emphasis will be placed on case studies that demonstrate the impact of computational methods on patient care and outcomes.