首页|University of Toronto Researcher Publishes New Study Findings on Machine Learnin g (Diffusion-Based Image Synthesis or Traditional Augmentation for Enriching Mus culoskeletal Ultrasound Datasets)
University of Toronto Researcher Publishes New Study Findings on Machine Learnin g (Diffusion-Based Image Synthesis or Traditional Augmentation for Enriching Mus culoskeletal Ultrasound Datasets)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting originating from Tor onto, Canada, by NewsRx correspondents, research stated, “Machine learning model s can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting musculoskeletal ultrasound im ages.” Financial supporters for this research include Novo Nordisk Health Care Ag. Our news correspondents obtained a quote from the research from University of To ronto: “However, machine learning requires lots of data, which poses significant challenges in medical imaging. Therefore, we explore two strategies for enrichi ng a musculoskeletal ultrasound dataset independent of these limitations: tradit ional augmentation and diffusion-based image synthesis. First, we generate augme nted and synthetic images to enrich our dataset. Then, we compare the images qua litatively and quantitatively, and evaluate their effectiveness in training a de ep learning model for detecting thickened synovium and knee joint recess distens ion. Our results suggest that synthetic images exhibit some anatomical fidelity, diversity, and help a model learn representations consistent with human opinion . In contrast, augmented images may impede model generalizability. Finally, a mo del trained on synthetically enriched data outperforms models trained on un-enri ched and augmented datasets.”
University of TorontoTorontoCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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