Transitional Asymmetric Non-local Neural Networks for Real-World Dirt Road Segmentation

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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Automatic MR Spinal Cord Segmentation by Hybrid Residual Attention-Aware Convolutional Neural Networks and Learning Rate Optimization on Real World Data

8th European Medical and Biological Engineering Conference - IFMBE Proceedings ◽

10.1007/978-3-030-64610-3_19 ◽

2020 ◽

pp. 158-168

Author(s):

A. Bueno Gómez ◽

A. Alberich-Bayarri ◽

I. Bosch ◽

J. Carreres Polo

Keyword(s):

Neural Networks ◽

Spinal Cord ◽

Convolutional Neural Networks ◽

Real World ◽

Learning Rate ◽

Real World Data ◽

World Data ◽

Rate Optimization

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Real World of Artificial Intelligence - A Review

Journal of Technology Management for Growing Economies ◽

10.15415/jtmge.2020.112005 ◽

2020 ◽

Vol 11 (2) ◽

pp. 41-47

Author(s):

Amandeep Kaur ◽

Madhu Dhiman ◽

Mansi Tonk ◽

Ramneet Kaur

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Natural Language Processing ◽

Face Recognition ◽

Language Processing ◽

Real World ◽

Review Paper ◽

General Concept ◽

Research Trends ◽

Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

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INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

International Journal of Computing ◽

10.47839/ijc.3.1.247 ◽

2014 ◽

pp. 8-20

Author(s):

Kurosh Madani

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Real World ◽

Industrial Applications ◽

Large Field ◽

Nonlinear Functions ◽

The Past ◽

Real World Applications ◽

Ann Models ◽

Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

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Deep Learning and Biomedical Engineering

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch022 ◽

2019 ◽

pp. 562-575

Author(s):

Suraj Sawant

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Real World ◽

Biomedical Engineering ◽

Input Data ◽

Reference Source ◽

Data Input ◽

Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

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Chapter 15. Human-Centered Concept Explanations for Neural Networks

10.3233/faia210362 ◽

2021 ◽

Author(s):

Chih-Kuan Yeh ◽

Been Kim ◽

Pradeep Ravikumar

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Case Studies ◽

Real World ◽

Deep Neural Networks ◽

Learning Models ◽

Real World Applications ◽

The Right ◽

Concept Activation ◽

Machine Learning Models

Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is making its predictions at the right level of abstraction. For example, providing importance weights to individual pixels in an image can only express which parts of that particular image is important to the model, but humans may prefer an explanation which explains the prediction by concept-based thinking. In this work, we review the emerging area of concept based explanations. We start by introducing concept explanations including the class of Concept Activation Vectors (CAV) which characterize concepts using vectors in appropriate spaces of neural activations, and discuss different properties of useful concepts, and approaches to measure the usefulness of concept vectors. We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.

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Rule Generation Methods Based on Logic Synthesis

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch184 ◽

2011 ◽

pp. 978-983

Author(s):

Marco Muselli

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Real World ◽

Inductive Reasoning ◽

Logic Synthesis ◽

Rule Generation ◽

Research Areas ◽

Real World Problem

One of the most relevant problems in artificial intelligence is allowing a synthetic device to perform inductive reasoning, i.e. to infer a set of rules consistent with a collection of data pertaining to a given real world problem. A variety of approaches, arising in different research areas such as statistics, machine learning, neural networks, etc., have been proposed during the last 50 years to deal with the problem of realizing inductive reasoning.

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