Learn From The Best: training AI using biological expert attention

人工智能（AI）正在对包括生物科学在内的许多学科产生巨大影响。它的力量是令人印象深刻的，它的采用将改变研究的性质，但目前它的发展方式有严重的实际局限性。尽管机器学习和人工智能最近取得了发展，但人类仍然拥有无与伦比的能力，只需看一张图片，就能准确地了解发生了什么。例如，人类专家能够看到一张有疾病症状的植物图片，并立即量化感染的严重程度。人工智能有望彻底改变生物图像分析，但到目前为止，只要有一小部分图像可供学习，专家人类的表现就会优于人工智能。人类和现代人工智能之间的一个重要区别是，我们被教导执行任务的方式。人类将学习图像的哪些部分是重要的，然后扫描图像以找到这些区域，然后再做出评分决定。人工智能通常使用标记数据进行训练，其中只有输出标签重要。人工智能不知道图像的哪些部分是重要的，或者它应该看哪里。当任务具有挑战性或只有小数据集可用时，这通常会导致性能低下。为了达到新闻中所记载的令人印象深刻的结果，当前的人工智能必须使用非常费力和低效的训练过程，这在真实的世界科学环境中通常是不切实际的。该项目将开发一种新的，更智能的方法来训练人工智能方法，使用类似于人类专家如何做出决策的机制。为了做到这一点，我们的人工智能将研究人类专家如何通过使用视线跟踪来了解专家在何时何地看过。其结果将是人工智能方法学会在正确的地方寻找，因此能够用比以前更少的训练数据做出更困难的评分决定。简而言之，我们认为，能够在做出决定之前查看正确位置的AI将比那些试图在不知道该查看位置的情况下简单地做出决定的AI更有效。在这个项目中，我们将首先开发在图像评分过程中捕获专家人类视线所需的硬件和软件方法。这些原始的凝视信息将使用新的算法进行处理，并与标记的分数一起沿着到一个新的深度学习AI系统中，引导它做出更明智的决策。人工智能将检查人类专家在提供图像标签时查看的图像位置，并将学习在复制相同任务时查看相同的位置。这是一种训练AI的新方法。最后，我们将构建一种新型的深度神经网络人工智能系统，它可以被这些额外的信息所引导，知道去哪里看，做什么。我们将在植物疾病的重要数据集上展示这项工作，但我们也相信这种方法将大大减少注释生命和生物医学科学所有领域的数据集所需的时间，同时产生更令人印象深刻和准确的AI结果。这可能代表了生物科学领域人工智能工具的采用和易用性的一步变化，允许在较小的图像数据集上进行更有效的训练。

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.