论文标题
零射击学习及其从自动驾驶汽车到COVID-19诊断的应用：评论
Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review
论文作者
论文摘要
在没有事先接受任何例子的情况下学习新概念，对象或新的医学疾病识别的挑战称为零射门学习（ZSL）。基于深度学习的方法（例如医学成像和其他现实世界应用）的主要问题之一是要求临床医生或专家准备的大型注释数据集以训练该模型。 ZSL以最少的人为干预而闻名，仅依靠先前已知或训练有素的概念以及当前现有的辅助信息。这使得ZSL适用于许多实际情况，从自动驾驶汽车的未知物体检测到医学成像和基于COVID-19胸部X射线（CXR）诊断等不可预见的疾病。我们介绍了一种名为“少数/一击学习”的小说和宽广的解决方案，并将ZSL问题的定义作为几乎没有学的极端情况。我们审查了基本面和零射门学习的挑战性步骤，包括最新的解决方案类别，以及我们推荐的解决方案，每种方法背后的动机，他们在每个类别中的优势都指导临床医生和AI研究人员，以根据其应用程序进行最佳技术和实践。然后，我们通过诱导医学和非医学图像，分裂的种类以及迄今为止提出的评估协议的不同数据集进行审查。最后，我们讨论了ZSL的最新应用和未来方向。我们旨在通过本文传达有用的直觉，以处理与人类学习方式更相似的复杂学习任务。我们主要关注当前现代但富有挑战性的时代的两种应用：应对COVID-19病例的早期和快速诊断，还鼓励读者使用ZSL开发其他类似的基于AI的自动检测/识别系统。
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. We introduce a novel and broaden solution called Few/one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. We then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection/recognition systems using ZSL.
