AI applications to medical images: From machine learning to deep learning

Abstract Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.

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3D Deep Learning on Medical Images: A Review

Sensors ◽

10.3390/s20185097 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5097 ◽

Cited By ~ 10

Author(s):

Satya P. Singh ◽

Lipo Wang ◽

Sukrit Gupta ◽

Haveesh Goli ◽

Parasuraman Padmanabhan ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Medical Imaging ◽

Medical Images ◽

Medical Image Analysis ◽

Disease Diagnosis ◽

Imaging Data ◽

Learning Models ◽

Processing Technologies ◽

3D Cnn

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

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Medical Image Analytics using Deep Learning (Convolutional Neural Networks)

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206291 ◽

2020 ◽

pp. 314-318

Author(s):

Santosh Bothe ◽

Mrunmayee Inamke ◽

Uttara Patidar ◽

Rutvi Ordia

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Medical Image ◽

Medical Images ◽

Early Stage ◽

Healthcare Organizations ◽

Medical Field ◽

Healthcare Facilities ◽

Technical Developments ◽

Human Eyes

Technical developments are being done in medical field. In order to improve medical results and healthcare facilities, machine learning and deep learning concepts are being used. Various experiments and efforts are done to detect diseases and provide platforms to provide better healthcare. Involvement of technology has made healthcare field more efficient and trustworthy. The ‘Medical Image Analytics’ is a machine learning as well as deep learning tool that would provide platform for processing medical images and extracting features not visible to human eye and provide accurate results and help to healthcare organizations. It strives to help healthcare organization for providing better healthcare facilities. This project is intended for use in various healthcare fields and organizations. Some features of the disease in medical images can be nit invisible or not clear to human eyes. Improper detection of features can lead to improper detection of diseases and may lead to failure or degradation in health and healthcare facilities. Thus, using techniques like deep learning and machine learning increases the detection of features in medical images. Also, it is helpful if diseases can be detected at an early stage and therefore, the project would aim to detect diseases at an early stage in future.

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A Tour of Unsupervised Deep Learning for Medical Image Analysis

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210127154257 ◽

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.

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CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis

Handbook of Research on the Role of Human Factors in IT Project Management - Advances in IT Personnel and Project Management ◽

10.4018/978-1-7998-1279-1.ch011 ◽

2020 ◽

pp. 164-172 ◽

Cited By ~ 1

Author(s):

Adekanmi Adeyinka Adegun ◽

Roseline Oluwaseun Ogundokun ◽

Marion Olubunmi Adebiyi ◽

Emmanuel Oluwatobi Asani

Keyword(s):

Machine Learning ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Skin Cancer ◽

Human Factor ◽

Medical Images ◽

Region Of Interest ◽

Machine Learning Techniques ◽

Age Related ◽

Learning Techniques

Machine learning techniques such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor-related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the diagnosis and monitoring of some diseases. Diseases such as skin cancer, age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis, and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.

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Deep Learning Based High-Resolution Remote Sensing Image classification

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.384 ◽

2017 ◽

Vol 7 (10) ◽

pp. 22

Author(s):

Sumit Kaur

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Image Classification ◽

Language Processing ◽

Object Perception ◽

Remote Sensing Image ◽

Research Area ◽

Remote Sensing Image Classification ◽

Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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