Computer vision is a technology that uses computer algorithms to perform vision tasks such as segmentation, detection, classification, recognition, and prediction. It has been applied in many fields including healthcare, transport, robotics, and communication due to its significant superiority. Compared to human eyes, computer vision makes it possible to observe things with a higher focus and bigger field of view than human eyes, meaning it can see more targets at the same time. Besides visible light, computer vision can observe things by other methods such as infrared light and x-ray imaging that are too difficult for human eyes to receive and process. Computer vision also comprehends high-dimensional and high-resolution images which contain more information than ordinary ones. Second, the advantage of computer vision is reflected in its performance of higher precision and speed when processing vision signals. Endless state-of-the-art algorithms have gifted computer vision with the power to detect and classify objects from input images, which has been demonstrated to beat human capacity in many aspects.

Due to these advantages, computer vision technology has been deployed in health and medical applications. There are a lot of circumstances like CT or MRI in medical fields that need vision processing work, for example, deep convolutional neural networks have been extensively used in the medical imaging processing of benign and malignant nodule classification of lung CT. Previously, traditional computer vision methods were applied to eliminate noise, enhance image quality, make hand-crafted features, or experiment with conventional machine learning algorithms like SVM or K-means. In recent years, with deep learning techniques, computer vision has dramatically improved its ability and played an increasingly vital role in the medical and health realm. However, there remain some challenges to be overcome, such as few-shot learning, or lacking annotation and ground truth.

This Special Issue invites original research and reviews on the topic of computer vision for human health and its applications.

Potential topics include but are not limited to the following:

• Computer vision-based healthcare systems and applications
• Computer aided diagnosis powered by computer vision or deep learning technology
• Automated segmentation of a region of interest in medical imaging
• Innovative computer vision detection methods suitable for medical usage
• Classification machine designed for medical imaging processing
• Survival prediction based on computer vision for diagnosis
• 3D reconstruction of human tissues, organs, and bodies for building medical models
• Emerging computational intelligence technology in clinical imaging process and analysis
• Emerging computational intelligence technology in medical signal processing