Publications and papers from and about the See Far project focusing on supporting the ageing workforce with vision deficiencies in Europe.
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review
Received 7 May 2021,
Revised 12 June 2021,
Accepted 18 June 2021,
Available online 25 June 2021.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models’ performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease’s lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions.
Eye behaviour provides valuable information revealing one’s higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.
- IEEE Keywords
- Author Keywords
See Far project, together with other six projects have worked recently on the non-scientific paper regarding the role of AI technologies in working through COVID-19 and its aftermath. This close collaboration was led by
SmartWork project http://www.smartworkproject.eu/
And joint by:
See Far https://www.see-far.eu/
See Far project had an active effort and role to elaborate and write about the digital solutions and systems to the Covid – 19 implication in different work environments.
THE ROLE OF AI TECHNOLOGIES IN WORKING THROUGH COVID-19 AND ITS AFTERMATH
The main aims of the concerted paper are to:
- reflect and share about the COVID-19 implications to the work environments, now that teleworking turned into a main instrument and necessity for us all;
- understand how the digital solutions and systems could be developed, adapted, optimized or applied to better respond to the pandemic context challenges.
The contributions collected followed three proposed guiding questions, although the contributing partners were free to explore other relevant aspects, in their understanding:
- How can technology apply to the work environment be leveraged to respond to the emerging challenges raised by COVID-19?
- Are there changes to the actual priorities and needs, considering the pandemic situation?
- Is this an opportunity for projects such as SmartWork to underline the needs to introduce a digital revolution in the workplace?
During the next pages, all the different contributions are aligned. The individual contributions and opinions of SmartWork partners are constituting Chapter 1. Chapter 2 collects the contributions provided by the different projects that were funded under the same call.
Some similarities can be highlighted:
- The desire to leverage the existing knowledge to rapidly respond to the challenges of this new (even if hopefully temporary) era;
- The understanding of the challenges ahead and the will to overcome them collectively.
The collective first step is already the creation of this common paper. It is surely a positive sign for the future. But also some conclusions can be drawn:
- COVID-19 impacted the way we live, work and spend free time severely,
- It has effects on physical and mental health and wellbeing,
AI can play a great role in providing solutions, not only during the emergency but also in the long-term, and not only for the office workers but also for the more traditional industries.