No abstract

The main drawback of magnetic resonance imaging (MRI) represents the lack of a standard intensity scale. All observed numerical values are relative and can only be interpreted together with their context. Before feeding MRI data volumes to supervised learning segmentation procedures, their histograms need to be registered to each other, or in other words, they need a so-called normalization. The most popular histogram normalization technique used to assist brain MRI segmentation is the algorithm proposed by Ny úl et al in 2000, which aligns the histograms of a batch of MRI volumes without paying attention to possible focal lesions that might distort the histogram. Alternately, some recent works applied histogram normalization based on a simple linear transform, and reported achieving slightly better accuracy with them. This paper proposes to investigate, which is the most suitable method and parameter settings for histogram normalization to be performed before the segmentation of brain MRI images, separately in the cases of absence and presence of focal lesions.

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate—the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.

We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based on the theoretical background of nilpotent fuzzy logic and multi-criteria decision-making (MCDM). The learnable parameter has a semantic meaning indicating the level of compensation between input features. The neural network determines the parameters using gradient descent to find human-understandable relationships between input features. We demonstrate the utility and effectiveness of the model by successfully applying it to classification problems from the UCI Machine Learning Repository.

No abstract