A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

Abstract

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions

“…Deep learning architectures, and in particular CNNs, provide state-of-the-art performance in many visual recognition applications, such as image classification [14] and object detection [15], as well as assisted medical diagnosis [16]. In depression detection, deep learning architectures that process on videos typically exploit spatial and temporal information separately (e.g., by cascading a 2D CNN and then a recurrent NN), which deteriorate the modeling of spatio-temporal relationships [11], [17].…”

Crowdsourcing sensitive data using public displays—opportunities, challenges, and considerations

“…As briefly mentioned earlier, one visionary application domain of computer vision is health care and health monitoring applications in general. Much progress has already been made in detecting health conditions based on video material or image frames [52], and more is expected to rapidly follow. Such future applications, however, do not necessarily need to only rely on already available training data but can increasingly tap into real-time, ubiquitous data collection methods.…”

“…Then, the users are encouraged to augment the video with additional metadata by answering a few questions. There exist interesting scenarios that could be enabled by this type of data collection, such as training algorithms to detect and monitor health conditions-according to latest research, up to 30 different symptoms and medical conditions can be detected from observing human faces with a camera [52]. The key contributions of our work are as follows:…”

A Spatiotemporal Convolutional Neural Network for Automatic Pain Intensity Estimation from Facial Dynamics

“…A potential approach to automatic pain assessment is through the use of facial expression analysis. The human face is indeed a rich source for non-verbal information regarding the health condition (Thevenot et al 2017). Facial expression can be considered as a reflective and spontaneous reaction of painful experiences (Craig et al 2011).…”