Machine Learning for Scientific Images

Abstract:
In recent years, convolutional neural networks (CNNs) have proved successful in many imaging problems in a wide variety of application fields. However, it can be difficult to train and apply existing CNNs to scientific images, due to computational, mathematical, and practical issues. In this talk, I will discuss newly developed methods that are specifically designed for scientific images. These methods can accurately train with large image sizes and a limited amount of training data (or no training data at all), and can automatically adapt to various tasks without extensive hyperparameter tuning. The talk will include comparisons between the new methods and existing CNNs, some recent results for real-world scientific problems, and ideas for future work.

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