AI in Breast Cancer Segmentation
Cancer ROI
AI-Powered Tile Analysis
AI-Powered Tile Analysis
This heatmap was generated using WSIer in QuPath with the breast_tumor_resnet deep learning model. The algorithm automatically identifies and highlights regions of interest (ROI) within whole slide images (WSIs) that are likely to contain breast cancer tissue. This AI-powered segmentation aids pathologists by providing accurate and high-r
This heatmap was generated using WSIer in QuPath with the breast_tumor_resnet deep learning model. The algorithm automatically identifies and highlights regions of interest (ROI) within whole slide images (WSIs) that are likely to contain breast cancer tissue. This AI-powered segmentation aids pathologists by providing accurate and high-resolution tumor localization, streamlining diagnostic workflows.
AI-Powered Tile Analysis
AI-Powered Tile Analysis
AI-Powered Tile Analysis
This image showcases the tile-based processing workflow used in conjunction with AI models. Each tile represents a portion of the tissue slide analyzed for cellular and structural features. This grid-based analysis ensures comprehensive, high-resolution scanning of the entire slide, allowing our system to detect subtle morphological changes critical for accurate cancer diagnosis.
Cancer Mapping
AI-Powered Tile Analysis
Raw Whole Slide Image
This image reflects a cancer region segmented through AI-driven analysis, produced in collaboration with clinical partners. These joint efforts help validate and refine our models on real-world pathology data, ensuring accurate diagnostics across diverse patient populations.
Raw Whole Slide Image
AI-Powered Tile Analysis
Raw Whole Slide Image
This slide represents raw histopathological data used for training and community education. We use such images to promote understanding of pathology processes during outreach events and seminars, empowering patients and professionals alike with visual learning tools.