Learn From The Best: training AI using biological expert attention

向最优秀的人学习：利用生物专家的注意力训练人工智能

• 批准号: BB/T012129/1
• 负责人: Michael Pound
• 金额: $ 17.82万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT012129%2F1
• 关键词: Learn; Best; training; AI; using

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.

人工智能（AI）对包括生物科学在内的许多学科产生了巨大影响。它的力量令人印象深刻，采用将改变研究的性质，但目前开发的方式具有严重的实际限制。尽管最近在机器学习和AI方面取得了进展，但人类仍然具有仅查看图片并确切了解正在发生的事情的无与伦比的能力。例如，人类专家能够查看具有疾病症状的植物的图片，并立即量化感染的严重程度。 AI承诺会彻底改变生物图像分析，但截至今天，专家人类的表现只能给出一小部分图像可以从AI中学习。人将了解图像的哪些部分很重要，然后在做出得分决定之前扫描图像以找到这些区域。 AI通常使用标记的数据训练，其中只有输出标签很重要。 AI不知道图像的哪些部分很重要，或者应该在哪里看起来。当任务具有挑战性时，或者只有小数据集时，这通常会导致性能差。为了获得新闻中记录的令人印象深刻的结果，当前的AI必须使用非常费力和效率低下的培训过程，这些培训过程在现实世界的科学环境中通常是不切实际的。该项目将开发一种新的，更明智的方法来培训人工智能方法，采用类似的机制来采用人类专家如何做出决策的类似机制。为此，我们的人工智能将研究人类专家如何通过使用凝视跟踪来查看专家的外观以及何时何时处理相同的问题。结果将是学会在正确的位置进行观察的AI方法，因此能够使用比以前需要的训练数据更少的训练数据来做出更难得分的决策。简而言之，我们认为，在做出决定之前能够在正确的位置上看的AI要比试图在不知道哪里看的决定更有效。在该项目中，我们将首先开发出在图像评分期间捕捉专家人类凝视所必需的硬件和软件方法。这种原始的目光信息将使用新颖的算法处理，并将其融入一个新的深度学习AI系统以及标记的分数，并将其引导到更明智的决策中。 AI将在提供图像标签时在图像中检查的图像中的位置，并在复制相同任务时学会在相同的位置看。这是培训AI的新方法。最后，我们将构建一种新型的深神经网络AI系统，可以通过这些其他信息来指导，知道在哪里看以及要做什么。我们将在植物性疾病的重要数据集中演示这项工作，但是我们也相信这种方法将大大减少注释数据集所需的时间，以使各个生活领域和生物医学科学领域以及同一时间产生更具印象和精确的一项成果。这可能代表了在生物科学领域采用和易于使用AI工具的步骤变化，从而可以在较小的图像数据集上进行更有效的培训。

项目成果

Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies

• DOI: 10.1016/j.cviu.2023.103776
• 发表时间: 2021-11
• 期刊: Comput. Vis. Image Underst.
• 作者: Bowen Deng;A. French;Michael P. Pound