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Journal ArticleDOI

Artificial neural networks in medical diagnosis

01 Jan 2013-Journal of Applied Biomedicine (University of South Bohemia)-Vol. 11, Iss: 2, pp 47-58
TL;DR: The philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples are reviewed and discussed.
About: This article is published in Journal of Applied Biomedicine.The article was published on 2013-01-01 and is currently open access. It has received 665 citations till now. The article focuses on the topics: Medical diagnosis & Artificial neural network.
Citations
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Journal ArticleDOI
08 May 2019-Nature
TL;DR: An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
Abstract: Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

862 citations

Journal ArticleDOI
02 Jan 2019
TL;DR: This work proposes a new abstract domain which combines floating point polyhedra with intervals and is equipped with abstract transformers specifically tailored to the setting of neural networks, and introduces new transformers for affine transforms, the rectified linear unit, sigmoid, tanh, and maxpool functions.
Abstract: We present a novel method for scalable and precise certification of deep neural networks. The key technical insight behind our approach is a new abstract domain which combines floating point polyhedra with intervals and is equipped with abstract transformers specifically tailored to the setting of neural networks. Concretely, we introduce new transformers for affine transforms, the rectified linear unit (ReLU), sigmoid, tanh, and maxpool functions. We implemented our method in a system called DeepPoly and evaluated it extensively on a range of datasets, neural architectures (including defended networks), and specifications. Our experimental results indicate that DeepPoly is more precise than prior work while scaling to large networks. We also show how to combine DeepPoly with a form of abstraction refinement based on trace partitioning. This enables us to prove, for the first time, the robustness of the network when the input image is subjected to complex perturbations such as rotations that employ linear interpolation.

545 citations


Cites background from "Artificial neural networks in medic..."

  • ...Over the last few years, deep neural networks have become increasingly popular and have now started penetrating safety critical domains such as autonomous driving [Bojarski et al. 2016] and medical diagnosis [Amato et al. 2013] where they are often relied upon for making important decisions....

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  • ...2016] and medical diagnosis [Amato et al. 2013] where they are often relied upon for making important decisions....

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Journal ArticleDOI
01 Jun 2019-Cities
TL;DR: This paper reviews the urban potential of AI and proposes a new framework binding AI technology and cities while ensuring the integration of key dimensions of Culture, Metabolism and Governance which are known to be primordial in the successful integration of Smart Cities for the compliance to the Sustainable Development Goal 11 and the New Urban Agenda.

497 citations

Proceedings ArticleDOI
20 May 2019
TL;DR: This work presents a method to predict multiple possible trajectories of actors while also estimating their probabilities, and successfully tested on SDVs in closed-course tests.
Abstract: Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads, making it very difficult to create a fully generalizable system. To ensure safe and efficient operations, an autonomous vehicle is required to account for this uncertainty and to anticipate a multitude of possible behaviors of traffic actors in its surrounding. We address this critical problem and present a method to predict multiple possible trajectories of actors while also estimating their probabilities. The method encodes each actor’s surrounding context into a raster image, used as input by deep convolutional networks to automatically derive relevant features for the task. Following extensive offline evaluation and comparison to state-of-the-art baselines, the method was successfully tested on SDVs in closed-course tests.

470 citations

Journal ArticleDOI
17 Dec 2013-Sensors
TL;DR: A recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services and a number of key challenges have been outlined for data mining methods in health monitoring systems.
Abstract: The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

373 citations

References
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Journal ArticleDOI
TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.

2,622 citations


"Artificial neural networks in medic..." refers methods in this paper

  • ...Here we will give only a brief description of the learning process; more details are provided for example in the review by (Basheer and Hajmeer 2000)....

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TL;DR: A brief survey of the motivations, fundamentals, and applications of artificial neural networks, as well as some detailed analytical expressions for their theory.

1,418 citations


"Artificial neural networks in medic..." refers background in this paper

  • ...A review of various classes of neural networks can be found in (Aleksander and Morton 1995, Zupan and Gasteiger 1999)....

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Book
01 Jan 1999
TL;DR: This new edition of a best-seller offers a sound introduction to artificial neuronal networks--with insights into their architecture, functioning, and applications.
Abstract: From the Publisher: This new edition of a best-seller offers a sound introduction to artificial neuronal networks--with insights into their architecture, functioning, and applications. Well organized and clearly written, it offers a wealth of useful information and invaluable practical guidance on this growing new area of chemical research.

651 citations


"Artificial neural networks in medic..." refers background or methods in this paper

  • ...However, the most commonly used is back propagation (Zupan and Gasteiger 1999; Ahmed 2005)....

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  • ...Various transfer functions are available (Zupan and Gasteiger 1999); however, the most commonly used is the sigmoid one:...

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  • ...A review of various classes of neural networks can be found in (Aleksander and Morton 1995, Zupan and Gasteiger 1999)....

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Book
30 Dec 1989
TL;DR: This chapter discusses why neural nets are important, how they are improving, and how they can be improved in the real world.
Abstract: Introduction - Why neural nets? Principles and promises. The McCulloch and Pitts legacy. The hard learning problem. Making neurons. The secrets of Wisard. Multi-layer perceptrons. Dynamic networks. Variations. Neurocontrol. Varieties of pattern analysis. Developments in weightless systems. Trends and promises.

555 citations

Journal ArticleDOI
TL;DR: The results show that classifier performance deteriorates with even modest class imbalance in the training data and it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features.

510 citations


"Artificial neural networks in medic..." refers background in this paper

  • ...A decrease in the classification performance of the network is observed for imbalanced databases (those with a different number of cases for each class) (Mazurowski et al. 2008)....

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