Demystifying Deep Learning AI and Digital Pathology:
DEMOCRATIZING AI AND EMPOWERING THE PATHOLOGIST
Your one-stop workshop will help you to understand and deploy intelligent solutions with ‘Aiforia Create’ Deep Learning Pathology. With a hands on experience to ensure you are trained to achieve precise active annotations and understand how different annotation mistakes affect your results.
The hands-on part is run as a friendly competition and the lowest error rate walks away with a prize and bragging rights.
Join the discussion and share your thoughts and ideas with us and the invited guest speakers
Thomas Westerling-Bui, Ph.D. , Senior Scientist, Aiforia Inc
Digital Pathology for drug development in the age of unlimited analysis design possibilities
Automated analysis can improve precision and recall, as well as reduce inter- and intra-operator differences. This leads to increased effect size and likelihood to get a measure of the true effect size of an experimental drug treatment.
Gillian Beamer, Assistant Professor, Tufts University
End user developed Deep Learning algorithms for the analysis of Lung granulomas in experimental tuberculosis.
Exploring the utility of domain expert driven Deep Learning algorithms to study Host Response to virulent M. tuberculosis including algorithms to delineate and segment granuloma and necrosis in hematoxylin and eosin (H&E) stained lung tissue from M. tuberculosis-infected Diversity Outbred mice.
Peter Westcott, Postdoctoral fellow, Koch Institute for Integrative Cancer Research
Deep neural network for automatic histopathological analysis and grading of murine lung tumors
A demonstration that deep neural networks can be used for automated analysis and grading of preclinical models of lung cancer and an exploration of how this powerful technology has the potential to increase the throughput, sensitivity and reproducibility of hypothesis-driven studies of factors influencing tumor progression and immune response in mouse models of lung cancer.
Jean-Martin Lapointe, DVM, MSc, DACVP, Senior Pathologist, AstraZeneca, UK
Deep learning image analysis approaches for evaluation of NAFLD/NASH mouse model histopathology
Nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) are common human liver diseases. Subjective scoring systems devised to diagnose histopathologic features of NAFLD/NASH have been adapted to animal models, but such semi-quantitative data shows poor granularity and statistical applicability, so has limited research usefulness. Image analysis is crucial to obtain quantitative data, but has its own challenges in liver pathology. H&E-stained liver will show hepatocyte lipid as clear spaces, difficult to distinguish from processing-associated glycogen loss. Total collagen can be quantified by IHC, but fibrosis-associated and normal structural collagen cannot be easily differentiated. Biliary hyperplasia does not show sufficiently distinctive H&E features for image analysis detection. AI deep learning allows for discriminating features of interest in H&E or IHC stains. Algorithms were developed to quantify macrovesicular and microvesicular steatosis, lobular vs portal collagen, and bile ducts, providing the quantified, granular measurements necessary to evaluate NAFLD/NASH in animal models.
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Stewart Hotel makes every effort to meet your needs and make your stay just right. Our boutique hotel amenities and services are designed to provide a thoroughly relaxing experience in the city that doesn’t sleep. Feel free to indulge your senses to connect, relax, drink, eat, work, stay fit and experience New York City.
This fee applies to organisations offering services and/or business & technology solutions and also to business consultants.
Fee for practising pathologists, hospitals, medical doctors, university academics & NFP employees. Offer not applicable to employees from pharma or solution providers who should select an option below.
Standard registration for those not in the categories below