NeuroCellCentreDB: Exploring a Novel Dataset for Neuron-like Cell Centre Detection with Deep Neural Networks.

The manipulation and stimulation of cell growth is invaluable for neuroscience research such as brain-machine interfaces or applications of neural tissue engineering. For the implementation of such research avenues, in particular the analysis of cells' migration behaviour, and accordingly, the determination of cell positions on microscope images is essential, causing a current need for labour-intensive, manual annotation efforts of the cell positions. In an attempt towards automation of the required annotation efforts, we i) introduce NeuroCellCentreDB, a novel dataset of neuron-like cells on microscope images with annotated cell centres, ii) evaluate a common (bounding box-based) object detector, faster region-based convolutional neural network (FRCNN), for the task at hand, and iii) design and test a fully convolutional neural network, with the specific goal of cell centre detection.

HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest.

Airborne pollen grain detection from partially labelled data utilising semi-supervised learning.

Airborne pollen monitoring has been conducted for more than a century now, as knowledge of the quantity and periodicity of airborne pollen has diverse use cases, like reconstructing historic climates and tracking current climate change, forensic applications, and up to warning those affected by pollen-induced respiratory allergies. Hence, related work on automation of pollen classification already exists. In contrast, detection of pollen is still conducted manually, and it is the gold standard for accuracy.

Personalised depression forecasting using mobile sensor data and ecological momentary assessment.

Introduction: Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations.

Depression Diagnosis and Forecast based on Mobile Phone Sensor Data.

Previous studies have shown the correlation be-tween sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easier to access and less time-consuming. In particular, passive mobile phone data can be collected on a flexible time interval, thus detecting moment-by-moment psychological changes and helping achieve earlier interventions.