Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

Pituitary ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 273-293 ◽  
Author(s):  
Ashirbani Saha ◽  
Samantha Tso ◽  
Jessica Rabski ◽  
Alireza Sadeghian ◽  
Michael D. Cusimano
Keyword(s):  
Machine Learning ◽  
Current Literature ◽  
Pituitary Tumors ◽  
Imaging Analysis ◽  
Future Directions ◽  
Machine Learning Applications

Machine Learning Applications to Resting-State Functional MR Imaging Analysis

2017 ◽  
Vol 27 (4) ◽  
pp. 609-620 ◽  
Author(s):  
John M. Billings ◽  
Maxwell Eder ◽  
William C. Flood ◽  
Devendra Singh Dhami ◽  
Sriraam Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mr Imaging ◽  
Resting State ◽  
Imaging Analysis ◽  
Machine Learning Applications ◽  
Functional Mr Imaging

scholarly journals Machine learning applications in the diagnosis of leukemia: Current trends and future directions

2019 ◽  
Vol 41 (6) ◽  
pp. 717-725 ◽  
Author(s):  
Haneen T. Salah ◽  
Ibrahim N. Muhsen ◽  
Mohamed E. Salama ◽  
Tarek Owaidah ◽  
Shahrukh K. Hashmi
Keyword(s):  
Machine Learning ◽  
Future Directions ◽  
Current Trends ◽  
Machine Learning Applications

scholarly journals Machine learning in Governments: Benefits, Challenges and Future Directions

2021 ◽  
Vol 13 (1) ◽  
pp. 203-219
Author(s):  
Yulu Pi
Keyword(s):  
Machine Learning ◽  
Public Sector ◽  
Empirical Studies ◽  
Data Availability ◽  
Future Research ◽  
Future Directions ◽  
Computing Power ◽  
Technical Aspects ◽  
The Public ◽  
Machine Learning Applications

The unprecedented increase in computing power and data availability has signifi-cantly altered the way and the scope that organizations make decisions relying on technologies. There is a conspicuous trend that organizations are seeking the use of frontier technologies with the purpose of helping the delivery of services and making day-to-day operational deci-sions. Machine learning (ML) is the fastest growing and at the same time, the most debated and controversial of these technologies. Although there is a great deal of research in the literature related to machine learning applications, most of them focus on the technical aspects or pri-vate sector use. The governmental machine learning applications suffer the lack of theoretical and empirical studies and unclear governance framework. This paper reviews the literature on the use of machine learning by government, aiming to identify the benefits and challenges of wider adoption of machine learning applications in the public sector and to propose the direc-tions for future research.

scholarly journals Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yong Wang ◽  
Liang Zhang ◽  
Lin Qi ◽  
Xiaoping Yi ◽  
Minghao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Quantitative Imaging ◽  
Endocrine Tumors ◽  
Health It ◽  
Imaging Analysis ◽  
Clinical Cancer Research ◽  
Standard Data ◽  
Positron Emission ◽  
Machine Learning Applications

Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

scholarly journals Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

2021 ◽  
Vol 3 (2) ◽  
pp. 392-413
Author(s):  
Stefan Studer ◽  
Thanh Binh Bui ◽  
Christian Drescher ◽  
Alexander Hanuschkin ◽  
Ludwig Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Practical Experience ◽  
Special Focus ◽  
Close Monitoring ◽  
Machine Learning Applications ◽  
Project Organizations ◽  
Considerable Impact ◽  
Learning Development

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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