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A Survey on Nature-Inspired Medical Image Analysis : A Step Further in Biomedical Data Integration

Rundo Leonardo, Militello Carmelo, Vitabile Salvatore, Russo Giorgio, Sala Evis, Gilardi Maria Carla

Fundamenta Informaticae

2020

Vol. 171, nr 1-4

345--365

Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets — i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring — to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image analysis methods is surveyed, aiming at establishing a common platform for beneficial exchanges among computer scientists and clinicians. In particular, this review focuses on the main nature-inspired computational techniques applied to medical image analysis tasks, namely: physical processes, bio-inspired mathematical models, Evolutionary Computation, Swarm Intelligence, and neural computation. These frameworks, tightly coupled with Clinical Decision Support Systems, can be suitably applied to every phase of the clinical workflow. We show that the proper combination of quantitative imaging and healthcare informatics enables an in-depth understanding of molecular processes that can guide towards personalised patient care.

Directed evolution : a new metaheuristc for optimization

Rotar C., Iantovics L. B.

Journal of Artificial Intelligence and Soft Computing Research

2017

Vol. 7, No. 3

183--200

Recently, we have witnessed an infusion of calculating models based on models offered by nature, models with more or less fidelity to the original that have led to the development of various problem-solving computational procedures. Starting from the observation of natural processes at the macroscopic or microscopic level, various methods have been developed. Technological progress today allows the accelerated reproduction of natural phenomena in the laboratory, which is why a new niche has arisen in the landscape of nature-inspired methods. This niche is devoted to the emulation of artificial biological processes in computational problem-solving methods. This paper proposes a novel approach, which is to develop novel computational methods in the field of Natural Computing based on the semi-natural process, namely Directed Evolution. In the first step we explain Directed Evolution, defined as the artificial reproduction of the process of evolution in the laboratory in order to obtain performing biological entities. For computer scientists, this provide a strong source of inspiration in the search for efficient methods of optimization. The computational model that proposed here largely overlaps with the Directed Evolution protocol, and the results obtained in the numerical experiments confirm the viability of such techniques inspired by processes which are more artificial than natural. The paper describes a novel general algorithm, inspired by Directed Evolution, which is able to solve different optimization problems, such as single optimization, multiobjective optimization and combinatorial optimization problems.