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pmid: 29190576
As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical image segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical image segmentation interfaces.
070, Reproducibility of Results, Medical image segmentation, Models, Theoretical, Sensitivity and Specificity, 004, Interface metaphors, Peircean semiotics, Pattern Recognition, Automated, Machine Learning, Systems Integration, User-Computer Interface, Image Processing, Computer-Assisted, Human computer interaction, Algorithms
070, Reproducibility of Results, Medical image segmentation, Models, Theoretical, Sensitivity and Specificity, 004, Interface metaphors, Peircean semiotics, Pattern Recognition, Automated, Machine Learning, Systems Integration, User-Computer Interface, Image Processing, Computer-Assisted, Human computer interaction, Algorithms
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 21 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |