DISA

When? Friday October 18th 14-16
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/CLBLYvcFgFSXAkBr8 October 17th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Design of an intelligent wearable for activity and health – the DIWAH study – Patrick Bergman
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Weakly supervised learning for dendritic cells image-segmentation – Jorge Lazo
15.50 -16.00 Sum up and plan for the November seminar

Abstracts

Design of an intelligent wearable for activity and health – the DIWAH study – Patrick Bergman

The overarching goal of the DIWAH-study is to create algorithms that can be implemented in welfare technology, specifically wearables, for self-monitoring and use within in the healthcare system. By utilizing the computing power of artificial intelligence (AI), we will develop and validate algorithms to assess physical activity (PA), energy expenditure (EE), and blood pressure (BP) at an individual level. Building on that information will develop self-learning AI algorithms that provides tailored PA recommendations on the appropriate dose to optimize an individual’s BP in real-time and without human intervention. This is a new approach since it not only gives information of the outcomes separately but also on the effect of PA has on health in real time. In the long run the AI will learn from its user and be able to provide personalized tailored activity advice to optimize the individual’s health. This has not been studied before, but the field has now reached a point where the technology exists along with processing power and analytical tools. Therefore, it is time to fully explore the possibility of combining real-time data on PA, health-related outcomes, and AI, so that future citizens can maintain or improve their health by making informed decisions based on personal data. For the older individuals in society this is extra important since they are at a higher risk of developing physical, mental, and social health issues where a maintained or increased PA-level is known to prevent and reduce the effects of such health problems. From a societal perspective a change from a reactive to a proactive health care system is necessary considering the demographic shift towards an aging population and an expected increased load on the healthcare system. Thus, it is necessary to develop evidence-based strategies that can relieve the healthcare system by promoting health, preventing diseases, and treating people in the best possible way when they do get sick. However, the current wearables available do not produce valid output especially among elderly individuals, therefore in this proposal we will

1. Identify and adapt open-source wearables suitable for assessing PA, energy expenditure and blood pressure
2. Collect data from the wearables in a controlled environment, using AI develop algorithms and to compare them against golden standard method
3. Test the developed algorithms during free-living and compare them against golden standard method

Weakly supervised learning for dendritic cells image-segmentation – Jorge Lazo

Manual detection and classification of immune cells in In-Vivo Confocal Microscopy (IVCM) images is a highly time-consuming task, and prone to subjective decisions and levels of expertise; therefore, it becomes a bottleneck in the detection and assessment of different ophthalmic disorders.

In the last few years, Deep-Learning (DL) based methods have been explored for the task of dendritic-cell segmentation in IVCM images, with fully-supervised strategies as the most common ones. These methods, despite showing promising results, rely on the availability of a large amount of detailed pixel-level labels, necessary to train the DL models.

Weakly supervised image segmentation is a DL technique where models are trained to segment images into meaningful regions or objects using limited, imprecise, or incomplete labels. In contrast to fully supervised learning, where detailed pixel-level annotations (or “masks”) are provided for every image, weakly supervised methods rely on weaker forms of supervision. Foundation models have made their appearance in the DL landscape offering powerful feature extraction and transfer learning capabilities. Even though their performance in medical imaging applications is still far from optimal, they offer a powerful tool for generating pseudo-labels that could be used in weakly supervised set-ups for training specialized models.

By utilizing weak supervision, these models can reduce the burden of manual annotation in medical imaging while still achieving high performance in identifying and segmenting key structures like dendritic cells.