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Explainable Detection of Microplastics Using Transformer Neural Networks
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Transformer neural network models can detect and classify microplastics in images with high accuracy while providing explainable outputs that identify which visual features drive classification decisions. Automated detection tools like this are key to scaling up microplastic monitoring programs and generating the standardized data needed to quantify pollution trends globally.
Microplastics are environmental contaminants that put marine and aquatic ecosystems at serious risk. Monitoring microplastics is necessary to understand the level of microplastic pollution in our environment. However, the lack of a standard protocol for quantifying...
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This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.