Search

Segmentation of tumors in the ultrasound (US) images of the breast is a critical problem in medical imaging. Due to the poor quality of the US images and varying specifications of the US machines, the segmentation and classification of the abnormalities present difficulties even for trained radiologists. Nevertheless, the US remains one of the most reliable and inexpensive tests. Recently, an artificial life (ALife) model based on tracing agents and fusion of the US and the elasticity images (F-ALife) has been proposed and analyzed. Under certain conditions, F-ALife outperforms state-of-the-art including the selected deep learning (DL) models, deformable models, machine learning, contour grouping and superpixels. Apart from the improved accuracy, F-ALife requires smaller training sets. The strongest competitors of the F-ALife are hybrids of the DL with conventional models. However, the current DL methods require a large amount of data (thousands of annotated images), which often is not available. Moreover, the hybrids require that the conventional model is properly integrated into the DL. Therefore, we offer a new DL-based hybrid with ALife. It is characterized by a high accuracy, requires a relatively small dataset, and is capable of handling previously unseen data. The new ideas include (1) a special image mask to guide ALife. The mask is generated using DL and the distance transform, (2) modification of ALife for segmentation of the US images providing a high accuracy. (These ideas are motivated by the “vehicles” of Braitenberg (Vehicles, experiments in synthetic psychology, MIT Press, Cambridge, 1984) and ALife proposed in Karunanayake et al. (Pattern Recognit 108838, 2022), (3) a two-level genetic algorithm which includes training by an individual image and by the entire set of images. The training employs an original categorization of the images based on the properties of the edge maps. The efficiency of the algorithm is demonstrated on complex tumors. The method combines the strengths of the DL neural networks with the speed and interpretability of ALife. The tests based on the characteristics of the edge map and complexity of the tumor shape show the advantages of the proposed DL-ALife. The model outperforms 14 state-of-the-art algorithms applied to the US images characterized by a complex geometry. Finally, the novel classification allows us to test and analyze the limitations of the DL for the processing of the unseen data. The code is applicable to breast cancer diagnostics (Automated Breast Ultra Sound), US-guided biopsies as well as to  projects related to automatic breast scanners. A video demo is at https://tinyurl.com/3xthedff. [ABSTRACT FROM AUTHOR]

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work. [ABSTRACT FROM AUTHOR]

The present article introduces the system BioAnt, which is a computational simulation of a small colony of ants (up to 99 members) in which every ant relies on a biologically more plausible artificial neural networks as control mechanism for guidance. The environment, in which the ants are placed, is three-dimensional, consisting of the anthill, sugar, water, earth elevations, walls and predators. The ants’ foraging behavior was successfully implemented as well as some basic defense mechanisms. Typical sensors and actuators of ants were modeled and the efficiency of the connectionist approach has been validated by the comparison with a simple symbolical approach. Apart from several surprising results on technical details, which are reported, the present approach clearly demonstrates the feasibility of such an implementation with connectionist and biologically more plausible principles, offering promising perspectives as a basis for further artificial life systems. [ABSTRACT FROM AUTHOR]

The proposed method is implemented in three steps: first, when a variation in environment is perceived, agents take appropriate actions. Second, the behaviors are stimulated and controlled through communication with other agents. Finally, the most frequently stimulated behavior is adopted as a group behavior strategy. In this paper, two different reward models, reward model 1 and reward model 2, are applied. Each reward model is designed to consider the reinforcement or constraint of behaviors. In competitive agent environments, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained by reducing the reward values. The validity of this strategy is verified through simulation. [ABSTRACT FROM AUTHOR]

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents’ emergent behavior during simulation. [ABSTRACT FROM AUTHOR]

Recently, there has been a lot of interest in evolving controllers for both physically simulated creatures as well as for real physical robots. However, a range of different ANN architectures are used for controller evolution, and, in the majority of the work conducted, the choice of the architecture used is made arbitrarily. No fitness landscape analysis was provided for the underlying fitness landscape of the controller’s search space. As such, the literature remains largely inconclusive as to which ANN architecture provides the most efficient and effective space for searching the range of possible controllers through evolutionary methods. This represents the motivation for this paper where we compare the search space for four different types of ANN architecture for controller evolution through an information-theoretic analysis of the fitness landscape associated with each type of architecture. [ABSTRACT FROM AUTHOR]