78,632 research outputs found
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification
Graph-based neural network models are gaining traction in the field of
representation learning due to their ability to uncover latent topological
relationships between entities that are otherwise challenging to identify.
These models have been employed across a diverse range of domains, encompassing
drug discovery, protein interactions, semantic segmentation, and fluid dynamics
research. In this study, we investigate the potential of Graph Neural Networks
(GNNs) for medical image classification. We introduce a novel model that
combines GNNs and edge convolution, leveraging the interconnectedness of RGB
channel feature values to strongly represent connections between crucial graph
nodes. Our proposed model not only performs on par with state-of-the-art Deep
Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting
in reduced training time and data requirements. We compare our Graph
Convolutional Neural Network (GCNN) to pre-trained DNNs for classifying
MedMNIST dataset classes, revealing promising prospects for GNNs in medical
image analysis. Our results also encourage further exploration of advanced
graph-based models such as Graph Attention Networks (GAT) and Graph
Auto-Encoders in the medical imaging domain. The proposed model yields more
reliable, interpretable, and accurate outcomes for tasks like semantic
segmentation and image classification compared to simpler GCNN
Assessing User Expertise in Spoken Dialog System Interactions
Identifying the level of expertise of its users is important for a system
since it can lead to a better interaction through adaptation techniques.
Furthermore, this information can be used in offline processes of root cause
analysis. However, not much effort has been put into automatically identifying
the level of expertise of an user, especially in dialog-based interactions. In
this paper we present an approach based on a specific set of task related
features. Based on the distribution of the features among the two classes -
Novice and Expert - we used Random Forests as a classification approach.
Furthermore, we used a Support Vector Machine classifier, in order to perform a
result comparison. By applying these approaches on data from a real system,
Let's Go, we obtained preliminary results that we consider positive, given the
difficulty of the task and the lack of competing approaches for comparison.Comment: 10 page
A web-based teaching/learning environment to support collaborative knowledge construction in design
A web-based application has been developed as part of a recently completed research which proposed a conceptual framework to collect, analyze and compare different design experiences and to construct structured representations of the emerging knowledge in digital architectural design. The paper introduces the theoretical and practical development of this application as a teaching/learning environment which has significantly contributed to the development and testing of the ideas developed throughout the research. Later in the paper, the application of BLIP in two experimental (design) workshops is reported and evaluated according to the extent to which the application facilitates generation, modification and utilization of design knowledge
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