scholarly journals Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

2017 ◽  
Vol 30 (4) ◽  
pp. 449-459 ◽  
Author(s):  
Zeynettin Akkus ◽  
Alfiia Galimzianova ◽  
Assaf Hoogi ◽  
Daniel L. Rubin ◽  
Bradley J. Erickson
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Future Directions

scholarly journals MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3243 ◽  
Author(s):  
Nagaraj Yamanakkanavar ◽  
Jae Young Choi ◽  
Bumshik Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Brain Structure ◽  
State Of The Art ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Learning Methods ◽  
The Brain

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Characterization of Errors in Deep Learning-Based Brain MRI Segmentation

2017 ◽  
pp. 223-242
Author(s):  
Akshay Pai ◽  
Yuan-Ching Teng ◽  
Joseph Blair ◽  
Michiel Kallenberg ◽  
Erik B. Dam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Mri Segmentation

scholarly journals Fast Brain MRI Segmentation Using a Volumetric Deep Learning Approach

2019 ◽  
Author(s):  
Dennis Bontempi ◽  
Sergio Benini ◽  
Alberto Signoroni ◽  
Lars Muckli ◽  
Michele Svanera
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Approach ◽  
Mri Segmentation

Deep Learning Techniques and COVID-19 Drug Discovery: Fundamentals, State-of-the-Art and Future Directions

2021 ◽  
pp. 9-31 ◽  
Author(s):  
Mohammad Behdad Jamshidi ◽  
Ali Lalbakhsh ◽  
Jakub Talla ◽  
Zdeněk Peroutka ◽  
Sobhan Roshani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
State Of The Art ◽  
Future Directions ◽  
Learning Techniques

scholarly journals A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

2020 ◽  
Vol 6 (12) ◽  
pp. 131
Author(s):  
Stefanus Tao Hwa Kieu ◽  
Abdullah Bade ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Hoshang Kolivand
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Recent Work ◽  
Lung Diseases ◽  
Data Augmentation ◽  
State Of The Art ◽  
Medical Images ◽  
Disease Detection ◽  
Future Directions ◽  
Survey Paper

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

scholarly journals Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

2021 ◽  
Vol 18 (3) ◽  
pp. 1117
Author(s):  
Tarik Alafif ◽  
Abdul Muneeim Tehame ◽  
Saleh Bajaba ◽  
Ahmed Barnawi ◽  
Saad Zia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Diagnosis And Treatment ◽  
Future Directions ◽  
Health Community ◽  
The World ◽  
The Status ◽  
And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

scholarly journals Multi-agent reinforcement learning for character control

2021 ◽  
Author(s):  
Cheng Li ◽  
Levi Fussell ◽  
Taku Komura
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Computer Games ◽  
State Of The Art ◽  
Research Topic ◽  
Future Directions ◽  
Computer Animations ◽  
Survey Papers ◽  
Simultaneous Control ◽  
Multi Agent

AbstractSimultaneous control of multiple characters has been a research topic that has been extensively pursued for applications in computer games and computer animations, for applications such as crowd simulation, controlling two characters carrying objects or fighting with one another and controlling a team of characters playing collective sports. With the advance in deep learning and reinforcement learning, there is a growing interest in applying multi-agent reinforcement learning for intelligently controlling the characters to produce realistic movements. In this paper we will survey the state-of-the-art MARL techniques that are applicable for character control. We will then survey papers that make use of MARL for multi-character control and then discuss about the possible future directions of research.

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