5,850 research outputs found
Visual parameter optimisation for biomedical image processing
Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality
output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple
input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships
between input and output.
Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by
integrating input and output, and by supporting exploration of their relationships. We discuss its application to a
colour deconvolution technique for stained histology images and show how it enabled a domain expert to
identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify
deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying
assumption about the algorithm.
Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs
in biomedical image processing that is not supported by previous analysis software. The analysis supported by our
method is not feasible with conventional trial-and-error approaches
Structured computer-based training in the interpretation of neuroradiological images
Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness
What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature
In this article, we conduct data mining to discover the countries,
universities and companies, produced or collaborated the most research on
Covid-19 since the pandemic started. We present some interesting findings, but
despite analysing all available records on COVID-19 from the Web of Science
Core Collection, we failed to reach any significant conclusions on how the
world responded to the COVID-19 pandemic. Therefore, we increased our analysis
to include all available data records on pandemics and epidemics from 1900 to
2020. We discover some interesting results on countries, universities and
companies, that produced collaborated most the most in research on pandemic and
epidemics. Then we compared the results with the analysing on COVID-19 data
records. This has created some interesting findings that are explained and
graphically visualised in the article
Digital service analysis and design : the role of process modelling
Digital libraries are evolving from content-centric systems to person-centric systems. Emergent services are interactive and multidimensional, associated systems multi-tiered and distributed. A holistic perspective is essential to their effective analysis and design, for beyond technical considerations, there are complex social, economic, organisational, and ergonomic requirements and relationships to consider. Such a perspective cannot be gained without direct user involvement, yet evidence suggests that development teams may be failing to effectively engage with users, relying on requirements derived from anecdotal evidence or prior experience. In such instances, there is a risk that services might be well designed, but functionally useless. This paper highlights the role of process modelling in gaining such perspective. Process modelling challenges, approaches, and success factors are considered, discussed with reference to a recent evaluation of usability and usefulness of a UK National Health Service (NHS) digital library. Reflecting on lessons learnt, recommendations are made regarding appropriate process modelling approach and application
A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].
Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data.
In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical
explanation of the brain map, for data visualisation, classification, and clustering is presented
Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence
I present a framework based on the concepts of diversity and coherence for
the analysis of knowledge integration and diffusion. Visualisations that help
understand insights gained are also introduced. The key novelty offered by this
framework compared to previous approaches is the inclusion of cognitive
distance (or proximity) between the categories that characterise the body of
knowledge under study. I briefly discuss the different methods to map the
cognitive dimension
- …