Automatically Designing CNN Architectures for Medical Image Segmentation

Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of time, expertise, and resources. To address this tedious problem, we propose a novel algorithm to optimally find hyperparameters of a deep network architecture automatically. We specifically focus on designing neural architectures for medical image segmentation task. Our proposed method is based on a policy gradient reinforcement learning for which the reward function is assigned a segmentation evaluation utility (i.e., dice index). We show the efficacy of the proposed method with its low computational cost in comparison with the state-of-the-art medical image segmentation networks. We also present a new architecture design, a densely connected encoder-decoder CNN, as a strong baseline architecture to apply the proposed hyperparameter search algorithm. We apply the proposed algorithm to each layer of the baseline architectures. As an application, we train the proposed system on cine cardiac MR images from Automated Cardiac Diagnosis Challenge (ACDC) MICCAI 2017. Starting from a baseline segmentation architecture, the resulting network architecture obtains the state-of-the-art results in accuracy without performing any trial-and-error based architecture design approaches or close supervision of the hyperparameters changes.Comment: Accepted to Machine Learning in Medical Imaging (MLMI 2018

Peer-to-Peer Networks: A Language Theoretic Approach

In this article a modification of a grammar systems theoretic construction, the so-called network of parallel language processors, is proposed to describe the behaviour of peer-to-peer (P2P) systems. In our model, the language processors form teams, send and receive information through collective and individual filters. The paper deals with the dynamics of string collections. The connection between the growth function of a developmental system and the growth function of networks of parallel multiset string processors with teams of collective and individual filtering is also established

An active, ontology-driven network service for Internet collaboration

Web portals have emerged as an important means of collaboration on the WWW, and the integration of ontologies promises to make them more accurate in how they serve users’ collaboration and information location requirements. However, web portals are essentially a centralised architecture resulting in difficulties supporting seamless roaming between portals and collaboration between groups supported on different portals. This paper proposes an alternative approach to collaboration over the web using ontologies that is de-centralised and exploits content-based networking. We argue that this approach promises a user-centric, timely, secure and location-independent mechanism, which is potentially more scaleable and universal than existing centralised portals

Preface: 11th Workshop on Non-classical Models of Automata and Applications (NCMA 2019)

Holzer, M.; Sempere Luna, JM. (2021). Preface: 11th Workshop on Non-classical Models of Automata and Applications (NCMA 2019). RAIRO - Theoretical Informatics and Applications. 55:1-2. https://doi.org/10.1051/ita/2021009S125

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented