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Medical Imaging Using Machine Learning and Deep Learning

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Special Issue Editors

Dr. Rui Guo

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Beijing National Research Center for Information Science and Technology (BNRist), Institute for Precision Medicine, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: deep learning; artificial intelligence; machine learning; multiphysical data inversion

Dr. He-Ming Yao

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Guest Editor

Department of Mathematics, The University of Hong Kong, Hong Kong, China
Interests: computational science; AI and machine learning; nanomaterials; electromagnetics

Dr. Francesco Zardi

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Guest Editor

ELEDIA Research Center (ELEDIA@UniTN), Department of Civil, Environmental, and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
Interests: microwave imaging; inverse scattering; smart electromagnetic environment; clustered array architectures

Dr. Mengchu Wang

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Guest Editor

Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, Via Eudossiana, Rome, Italy
Interests: microwave imaging; inverse scattering; antenna design; dielectric properties’ measurement

Special Issue Information

Dear Colleagues,

Recent years have witnessed a rapid growth of interest in the development of intelligent imaging systems for medical purposes. Intelligent medical imaging is attractive for its high speed, super-resolution, and low cost. In particular, machine learning (ML) and deep learning (DL) techniques that seamlessly integrate big data and high-performance computing have largely facilitated the study of advanced medical imaging systems and their applications. So far, a wide range of work has shown the merit of ML/DL-based imaging systems as compared to conventional ones. Still, a considerable amount of challenges remain to be addressed in this field, concerning not only fundamental theory but also its clinical applications.

This Special Issue will be dedicated to intelligent medical imaging pipelines, including but not limited to the learning theory, smart system design, imaging methods, algorithms, signal and image processing techniques, with their applications to electromagnetic imaging/computed tomography (CT)/magnetic resonance imaging (MRI)/positron emission tomography (PET)/ultrasound (US), as well as multimodalities joint imaging.

Dr. Rui Guo
Dr. He-Ming Yao
Dr. Francesco Zardi
Dr. Mengchu Wang
Guest Editors

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Keywords

machine learning
deep learning
medical imaging
imaging methods
smart systems

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Research

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15 pages, 2585 KiB

Open AccessArticle

Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals

by Talha Iqbal, Adnan Elahi, William Wijns, Bilal Amin and Atif Shahzad

Appl. Sci. 2023, 13(5), 2950; https://doi.org/10.3390/app13052950 - 24 Feb 2023

Cited by 8 | Viewed by 2198

Abstract

Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature’s 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification. Full article

(This article belongs to the Special Issue Medical Imaging Using Machine Learning and Deep Learning)

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25 pages, 4918 KiB

Open AccessReview

Applying Deep Learning to Medical Imaging: A Review

by Huanhuan Zhang and Yufei Qie

Appl. Sci. 2023, 13(18), 10521; https://doi.org/10.3390/app131810521 - 21 Sep 2023

Cited by 2 | Viewed by 6229

Abstract

Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the diagnosis and treatment of diseases and how it has revolutionized the medical imaging field. Furthermore, we examine the most recent DL techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applications in medical imaging. Lastly, we provide insights into the future of DL in medical imaging, highlighting its potential advancements and challenges. Full article

(This article belongs to the Special Issue Medical Imaging Using Machine Learning and Deep Learning)

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