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From Collective Adaptive Systems to Human Centric Computation and Back: Spatial Model Checking for Medical Imaging

Authors: Belmonte G; Ciancia V; Latella D; Massink M;

From Collective Adaptive Systems to Human Centric Computation and Back: Spatial Model Checking for Medical Imaging

Abstract

Recent research on formal verification for Collective Adaptive Systems (CAS) pushed advancements in spatial and spatio-temporal model checking, and as a side result provided novel image analysis methodologies, rooted in logical methods for topological spaces. Medical Imaging (MI) is a field where such technologies show potential for ground-breaking innovation. In this position paper, we present a preliminary investigation centred on applications of spatial model checking to MI. The focus is shifted from pure logics to a mixture of logical, statistical and algorithmic approaches, driven by the logical nature intrinsic to the specification of the properties of interest in the field. As a result, novel operators are introduced, that could as well be brought back to the setting of CAS.

In Proceedings FORECAST 2016, arXiv:1607.02001

Country
Italy
Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, F.4.1 modal logic, Computer Vision and Pattern Recognition (cs.CV), Software/Program Verification, Computer Science - Computer Vision and Pattern Recognition, D.2.4 model checking, QA75.5-76.95, Model logics, Logic in Computer Science (cs.LO), Medical Imaging, Spatial Model-checking, Spatial Logics, Electronic computers. Computer science, QA1-939, Mathematical Logic, D.2.4 model checking; F.4.1 modal logic; J.3 medical information systems, Mathematics, J.3 medical information systems

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Top 10%
Top 10%
Green
gold