Validating a Model of Colon Colouration Using an Evolution Strategy with Adaptive Approximations

Hidović, Džena; Rowe, Jonathan E.

doi:10.1007/978-3-540-24855-2_112

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…The model is used to simulate reflectance spectra for ranges of tissue optical properties. These spectra are fitted to experimental results in a fast iterative process that uses the Kubelka-Munk approximation of diffuse reflectance as part of the optimisation process ( Hidović and Rowe, 2004 ; Hidović-Rowe and Claridge, 2005 ; Claridge et al., 2007 ). Wirkert et al.…”

Section: Spectral Image Analysismentioning

confidence: 99%

Surgical spectral imaging

Clancy

Jones

Maier-Hein

et al. 2020

Medical Image Analysis

111

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Highlights Wider sensor availability and miniaturisation are pushing speed/resolution limits. Small surgical datasets exist in many specialities but no standard format. Data-driven analysis avoids modelling, improves speed, addresses uncertainty. RGB-based functional imaging could exploit existing cameras, chip-on-tip devices. Clinical validation with standardised devices and data needed for translation.

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Section: Spectral Image Analysismentioning

confidence: 99%

Surgical spectral imaging

Clancy

Jones

Maier-Hein

et al. 2020

Medical Image Analysis

111

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“…Some examples of applications are economic validation of capital projects [37] (Economics), prediction of fibrinogen absorption onto polymer surfaces [38] (Chemistry), electromagnetic simulations (Electronics) [33] and modeling colon coloration [39] (Medicine).…”

Section: Motivationmentioning

confidence: 99%

Automatic Approximation of Expensive Functions with Active Learning

Gorissen

Crombecq

Couckuyt

et al. 2009

Studies in Computational Intelligence

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Abstract. The use of computer simulations has become a viable alternative for real-life controlled experiments. Due to the computational cost of these high fidelity simulations, surrogate models are often employed as an approximation for the original simulator. Because of their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, optimization, visualization and sensitivity analysis. Additionally, multiple surrogate models can be chained together to model large scale systems where the direct use of the original expensive simulators would be too cumbersome. Many surrogate model types, such as neural networks, support vector machines and rational models have been proposed, and many more techniques have been developed to minimize the number of expensive simulations required to train a sufficiently accurate surrogate model. In this chapter, we present a fully automated and integrated global surrogate modeling methodology for regression modeling and active learning that readily enables the adoption of advanced global surrogate modeling methods by application scientists. The work brings together insights from distributed systems, artificial intelligence, and modeling & simulation, and has applications in a very wide range of fields. The merits of this approach are illustrated with several examples, and several surrogate model types.

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“…In fact a continuous version of this algorithm has been used successfully on a number of benchmark and realworld problems [6,3]. One would like to know, however, if one couldn't do better by an even cleverer choice of probability distribution.…”

Section: Theorem 4 the (1 + 1)-ea Using The Harmonic Distribution Tamentioning

confidence: 99%

Precision, Local Search and Unimodal Functions

et al. 2009

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We investigate the effects of precision on the efficiency of various local search algorithms on 1-D unimodal functions. We present a (1 + 1)-EA with adaptive step size which finds the optimum in O(log n) steps, where n is the number of points used. We then consider binary and Gray representations with single bit mutations. The standard binary method does not guarantee locating the optimum, whereas using Gray code does so in O((log n)2 ) steps. A (1 + 1)-EA with a fixed mutation probability distribution is then presented which also runs in O((log n)2 ). Moreover, a recent result shows that this is optimal (up to some constant scaling factor), in that there exist unimodal functions for which a lower bound of Ω((log n)2 ) holds regardless of the choice of mutation distribution. Finally, we show that it is not possible for a black box algorithms to efficiently optimise unimodal functions for two or more dimensions (in terms of the precision used).

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Validating a Model of Colon Colouration Using an Evolution Strategy with Adaptive Approximations

Cited by 8 publications

References 9 publications

Surgical spectral imaging

Surgical spectral imaging

Automatic Approximation of Expensive Functions with Active Learning

Precision, Local Search and Unimodal Functions

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