29 Deep Learning-Based Automatic ... Computer Vision News Exploring AgNORs as a viable assessment tool is no simple task. Jonathan and Marc worked closely with collaborators in veterinary pathology, including Christof Bertram, who was passionate about the topic and instrumental in pushing it forward. The team used supervised learning to establish a substantial canine dataset with roughly 23,000 annotated cells. As AgNORs are primarily counted under light microscopes, limited information exists regarding how accurate humans are at carrying out the task. The researchers conducted a human rater experiment, enlisting pathologists for a comparative study, allowing a unique assessment of the algorithm’s performance against human raters, which resulted in valuable insights into the reliability of both methods.
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