scholarly journals Multiple Kernel Learning approach for Medical Image Analysis

2017 ◽  
Author(s):  
Nisar Wani ◽  
Khalid Raza
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Multiple Kernel Learning ◽  
Image Data ◽  
Heterogeneous Data ◽  
Image Features ◽  
Kernel Learning ◽  
Multiple Kernel

ABSTRACTComputer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high dimensional and heterogeneous data. In this chapter, after presenting a breif description about nature of medical images, image features and basics in machine learning and kernel methods, we present the application of multiple kernel learning algorithms for medical image analysis.

scholarly journals A Tour of Unsupervised Deep Learning for Medical Image Analysis

2021 ◽  
Vol 17 ◽  
Author(s):  
Khalid Raza ◽  
Nripendra Kumar Singh
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Heterogeneous Data ◽  
Learning Techniques ◽  
Deep Boltzmann Machine ◽  
Unsupervised Deep Learning

Background: Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available. Objectives: The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and its other variants, Restricted Boltzmann machines (RBM), Deep belief networks (DBN), Deep Boltzmann machine (DBM), and Generative adversarial network (GAN). Further, future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed. Conclusion: Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

scholarly journals Efficient CNN for Lung Cancer Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 3499-3505
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Image Analysis ◽  
Cancer Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Filter Width ◽  
Early Disease Detection ◽  
Fully Connected ◽  
Lung Cancer Detection

The machine learning based solutions for medical image analysis are successful in detection of wide variety of anomalies in imaging procedures. The aim of the medical image analysis systems based on machine learning methods is to improve the accuracy and minimize the detection time. The aim in turn contributes to early disease detection and extending the patient life. This paper presents an efficient CNN (EFFI-CNN) for Lung cancer detection. EFFI-CNN consists of seven CNN layers (i.e. Convolution layer, Max-Pool layer, Convolution layer, Max-Pool layer, fully connected layer, fully connected layer and Soft-Max layer). EFFI-CNN uses lung CT scan images from LIDC-IDRI and Mendeley data sets. EFFI-CNN has a unique combination of CNN layers with parameters (Depth, Height, Width, filter Height and filter width).

Multiple kernel learning by empirical target kernel

2019 ◽  
Vol 18 (02) ◽  
pp. 1950058
Author(s):  
Peiyan Wang ◽  
Dongfeng Cai
Keyword(s):  
Multiple Kernel Learning ◽  
Heterogeneous Data ◽  
Classification Performance ◽  
Training Data ◽  
Kernel Learning ◽  
Kernel Weights ◽  
Heterogeneous Data Sources ◽  
Multiple Kernel ◽  
Global Target ◽  
Weighted Kernel

Multiple kernel learning (MKL) aims at learning an optimal combination of base kernels with which an appropriate hypothesis is determined on the training data. MKL has its flexibility featured by automated kernel learning, and also reflects the fact that typical learning problems often involve multiple and heterogeneous data sources. Target kernel is one of the most important parts of many MKL methods. These methods find the kernel weights by maximizing the similarity or alignment between weighted kernel and target kernel. The existing target kernels implement a global manner, which (1) defines the same target value for closer and farther sample pairs, and inappropriately neglects the variation of samples; (2) is independent of training data, and is hardly approximated by base kernels. As a result, maximizing the similarity to the global target kernel could make these pre-specified kernels less effectively utilized, further reducing the classification performance. In this paper, instead of defining a global target kernel, a localized target kernel is calculated for each sample pair from the training data, which is flexible and able to well handle the sample variations. A new target kernel named empirical target kernel is proposed in this research to implement this idea, and three corresponding algorithms are designed to efficiently utilize the proposed empirical target kernel. Experiments are conducted on four challenging MKL problems. The results show that our algorithms outperform other methods, verifying the effectiveness and superiority of the proposed methods.

An Overview of Machine Learning in Medical Image Analysis

2017 ◽  
pp. 36-58 ◽  
Author(s):  
Anand Narasimhamurthy
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Imaging ◽  
Health Informatics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Applications Of Machine Learning

Medical image analysis is an area which has witnessed an increased use of machine learning in recent times. In this chapter, the authors attempt to provide an overview of applications of machine learning techniques to medical imaging problems, focusing on some of the recent work. The target audience comprises of practitioners, engineers, students and researchers working on medical image analysis, no prior knowledge of machine learning is assumed. Although the stress is mostly on medical imaging problems, applications of machine learning to other proximal areas will also be elucidated briefly. Health informatics is a relatively new area which deals with mining large amounts of data to gain useful insights. Some of the common challenges in health informatics will be briefly touched upon and some of the efforts in related directions will be outlined.

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

2021 ◽  
Author(s):  
◽  
Mahdieh Shabanian ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Neurological Disease ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Disease State ◽  
Institutional Review Board ◽  
Institutional Review

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases.

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