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2018 GTC Washington DC

DC8119 - Deep Learning for Streamlined Image Interpretation in Cancer and Biomedical Research

Session Speakers
Session Description

This talk will explain how high quality image segmentation is critical in biomedical image interpretation for accurate diagnosis and/or assessment of a disease. The FNLCR IVG aims to integrate deep learning into image analysis workflows to produce quantitative, accurate, high throughput, and reproducible results to streamline image interpretation. We trained CNNs for mice tumor segmentation on MRI images for radiomics studies on patient derived xenograft (PDX) models. We trained CNNs and developed software infrastructures for feature quantification of whole slide histology images applied to collagen network analysis and stroma segmentation. Key features include the ability to annotate whole slides, incorporate multiplexed features, and providing an interactive interface for "human-in-the-loop" review and feedback.


Additional Information
AI in Healthcare
AI in Healthcare, Computer Vision/Machine Vision
Higher Education / Research
All technical
Talk
50 minutes
Session Schedule