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Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty.

Sensors (Basel, Switzerland)
In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by analyzing the similarity of their spatial EEG features. We present an analysis, demonstrating the model'...

LM-Net: A light-weight and multi-scale network for medical image segmentation.

Computers in biology and medicine
Current medical image segmentation approaches have limitations in deeply exploring multi-scale information and effectively combining local detail textures with global contextual semantic information. This results in over-segmentation, under-segmentat...

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture.

International journal of language & communication disorders
BACKGROUND: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early dete...

MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into...

A knowledge graph-based data harmonization framework for secondary data reuse.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and ana...

HeNeCOn: An ontology for integrative research in Head and Neck cancer.

International journal of medical informatics
BACKGROUND: Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, wh...

Prompt tuning for parameter-efficient medical image segmentation.

Medical image analysis
Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e.g. for...

Filter pruning for convolutional neural networks in semantic image segmentation.

Neural networks : the official journal of the International Neural Network Society
The remarkable performance of Convolutional Neural Networks (CNNs) has increased their use in real-time systems and devices with limited resources. Hence, compacting these networks while preserving accuracy has become necessary, leading to multiple c...

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation.

Computers in biology and medicine
Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers' model in the field of computer vision, researchers have begun to gradually explore the appl...

Context-based refinement of mappings in evolving life science ontologies.

Journal of biomedical semantics
BACKGROUND: Biomedical computational systems benefit from ontologies and their associated mappings. Indeed, aligned ontologies in life sciences play a central role in several semantic-enabled tasks, especially in data exchange. It is crucial to maint...