Invited lectures Machine learning meets medical imaging: Learning and discovery of clinically useful information from images

2013 ◽  
pp. 21-26
Keyword(s):  
Machine Learning ◽  
Medical Imaging

Machine learning in medical imaging

2020 ◽  
pp. 167-196 ◽  
Author(s):  
Ashnil Kumar ◽  
Lei Bi ◽  
Jinman Kim ◽  
David Dagan Feng
Keyword(s):  
Machine Learning ◽  
Medical Imaging

scholarly journals Machine Learning in Healthcare Communication

Encyclopedia ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 220-239
Author(s):  
Sarkar Siddique ◽  
James C. L. Chow
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Therapy ◽  
Medical Imaging ◽  
Health Education ◽  
Human Intelligence ◽  
Computer Algorithms ◽  
Dialogue Management ◽  
Topical Review ◽  
Healthcare Communication

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020

2021 ◽  
Vol 18 ◽  
Author(s):  
Nosaiba Al-Ryalat ◽  
Lna Malkawi ◽  
Ala'a Abu Salhiyeh ◽  
Faisal Abualteen ◽  
Ghaida Abdallah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Nuclear Medicine ◽  
Medical Imaging ◽  
Core Collection ◽  
Web Of Science ◽  
Vos Viewer ◽  
Literature Mapping ◽  
Link Strength ◽  
Linkage Strength

Objectives: Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020, analyzing the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping, along with a bibliometric analysis for publications. Methods: We performed a search on Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, which represents major topics discussed. Of the resulting topics, literature map created, and linkage analysis done. Results: A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords, only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while “computed tomography” has the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by “radiotherapy, and “deep and machine learning”. The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included “COVID-19” or “coronavirus” in their title, including the top cited paper. Conclusion: While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represent the topic of top cited articles in 2020 radiology publications.

scholarly journals Machine Learning in Medical Imaging

2010 ◽  
Vol 27 (4) ◽  
pp. 25-38 ◽  
Author(s):  
Miles Wernick ◽  
Yongyi Yang ◽  
Jovan Brankov ◽  
Grigori Yourganov ◽  
Stephen Strother
Keyword(s):  
Machine Learning ◽  
Medical Imaging

scholarly journals Effect of GAN-based image standardization on MR knee bone-tissue classification performance

10.29007/d18s ◽  
2020 ◽  
Author(s):  
Vincent Jaouen ◽  
Guillaume Dardenne ◽  
Florent Tixier ◽  
Éric Stindel ◽  
Dimitris Visvikis
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Bone Tissue ◽  
Pulse Sequences ◽  
Image Synthesis ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Pool Data ◽  
Tissue Classification ◽  
High Definition

Due to their sensitivity to acquisition parameters, medical images such as magnetic resonance images (MRI), Positron Emission tomography (PET) or Computed Tomography (CT) images often suffer from a kind of variability unrelated to diagnostic power, often known as the center effect (CE). This is especially true in MRI, where units are arbitrary and image values can strongly depend on subtle variations in the pulse sequences [1]. Due to the CE it is particularly difficult in various medical imaging applications to 1) pool data coming from several centers or 2) train machine learning algorithms requiring large homogeneous training sets. There is therefore a clear need for image standardization techniques aiming at reducing this effect.Considerable improvements in image synthesis have been achieved over the recent years using (deep) machine learning. Models based on generative adversarial neural networks (GANs) now enable the generation of high definition images capable of fooling the human eye [2]. These methods are being increasingly used in medical imaging for various cross-modality (image-to-image) applications such as MR to CT synthesis [3]. However, they have been seldom used for the purpose of image standardization, i.e. for reducing the CE [4].In this work, we explore the potential advantage of embedding a standardization step using GANs prior to knee bone tissue classification in MRI. We consider image standardization as a within-domain image synthesis problem, where our objective is to learn a mapping between a domain D constituted of heterogeneous images and a reference domain R showing one or several images of desired image characteristics.Preliminary results suggest a beneficial impact of such a standardization step on segmentation performance.

scholarly journals On the Methods for Detecting Brain Tumor from MRI images

2020 ◽  
Vol 9 (9s) ◽  
pp. 37-44
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Imaging ◽  
Tumor Detection ◽  
Gray Level ◽  
Wide Spread ◽  
Emerging Trends ◽  
Day By Day ◽  
High Diversity

Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples. Automatic detection has got wide spread acclaim because the manual detection by experts is time consuming and prone to error in judgment. Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. Here we present a review of few methods from simple thresholding to advanced deep learning methods for segmentation of tumor from MRI data. The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. The results of various methods are compared to find the best methods available. As medical imaging methods have improving day by day this review will help to understand emerging trends in brain tumor detection.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

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