Learn From The Best: training AI using biological expert attention

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Artificial intelligence (AI) is having a massive impact on many disciplines, including biological science. Its power is impressive and its adoption will change the nature of research, but at the moment the way it is developed has severe practical limitations. Despite the recent developments in machine learning and AI, humans still possess an unrivalled ability to just look at a picture, and understand exactly what is going on. A human expert is able to look at a picture of a plant with disease symptoms, for example, and immediately quantify the severity of the infection. AI promises to revolutionise bioimage analysis, but as of today an expert human will outperform an AI given only a small set of images to learn from.One important difference between humans and modern AI is the way we are taught to perform a task. A human will learn which parts of an image are important, then scan the images to find these areas before coming to reach a scoring decision. AI is typically trained using labeled data, where only the output label matters. An AI does not know which parts of the image are important, or where it should look. This often leads to poor performance when the task is challenging, or when only small datasets are available. To achieve the impressive results as has been documented in the news, current AI must use very laborious and inefficient training processes, which are often impractical in a real world scientific setting.This project will develop a new, smarter way to train artificial intelligence methods, using similar mechanisms to how human experts make decisions. To do this, our AI will study how human experts approach the same problems by using gaze tracking to see where an expert looked, and when. The result will be AI methods that learn to look in the right places, and so are able to take more difficult scoring decisions with less training data than they would previously need. Put simply, we believe that an AI that is able to look in the correct places before making a decision will be more effective than one that attempts to simply make a decision without knowing where to look.In this project we will first develop the hardware and software approaches necessary to capture expert human gaze during image scoring. This raw gaze information will be processed using novel algorithms, and fed into a new deep learning AI system along with the labelled scores, guiding it towards more informed decision making. The AI will examine where in the images human experts looked when providing an image label, and will learn to look in those same places when it replicates the same task. This is a new approach to training AI. Finally, we will build a new type of deep neural network AI system that can be guided by this additional information, knowing where to look, and what to do.We will demonstrate this work on important datasets of plant disease, but we also believe this approach will massively reduce the time required to annotate datasets across all fields of life and biomedical science, and at the same time produce even more impressive and accurate AI results. This could represent a step-change in the adoption and ease of use of AI tools in the world of bioscience, allowing for more efficient training on smaller image datasets.