Electronics

Journal Browser

► Journal Browser

Machine Learning Applied to Medical Image Analysis

Share This Special Issue

Special Issue Editors

Special Issue Information

Dear Colleagues,

In the last decade, machine learning (ML) techniques have been proven to be extremely powerful in many fields of computer vision and image processing, addressing several classical problems in a more effective and efficient way than ever before. Leveraging the massive growth in dimension, resolution, complexity, and heterogeneity (multimodality) of medical image datasets, ML seems to be a valuable technology to help clinicians in understanding human health and disease. ML plays an increasingly relevant role to make sense of imaging data to identify signatures of disorders and, when the temporal information is available, to decode the dynamic activity. The era of “big imaging data” raises new challenges for finding population patterns, making predictions, and extracting relevant biomarkers.

This Special Issue is aimed at presenting the state-of-the-art, current challenges and future trends for the successful application of ML to medical imaging. Original contributions considering recent findings in theory, methodologies, and applications in the field of ML for medical image analysis are welcome. Potential topics include but are not limited to:

Medical image segmentation;
Shape modeling of anatomical structures;
Multimodal medical image registration;
Lesion detection;
Temporal prediction of disease evolution;
Brain connectivity;
Transfer learning and domain adaptation.

Dr. Silvia Francesca Storti
Dr. Francesco Setti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Machine learning methods
Image and signal analysis
(Dynamic) functional imaging
Multimodal data analysis
Deep learning
Artificial neural networks

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

28 pages, 37326 KiB

Open AccessArticle

Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification

by Ganesan Jothi, Hannah H. Inbarani, Ahmad Taher Azar, Anis Koubaa, Nashwa Ahmad Kamal and Khaled M. Fouad

Electronics 2020, 9(5), 794; https://doi.org/10.3390/electronics9050794 - 12 May 2020

Cited by 13 | Viewed by 2345

Abstract

Acute lymphoblastic leukemia is a well-known type of pediatric cancer that affects the blood and bone marrow. If left untreated, it ends in fatal conditions due to its proliferation into the circulation system and other indispensable organs. All over the world, leukemia primarily attacks youngsters and grown-ups. The early diagnosis of leukemia is essential for the recovery of patients, particularly in the case of children. Computational tools for medical image analysis, therefore, have significant use and become the focus of research in medical image processing. The particle swarm optimization algorithm (PSO) is employed to segment the nucleus in the leukemia image. The texture, shape, and color features are extracted from the nucleus. In this article, an improved dominance soft set-based decision rules with pruning (IDSSDRP) algorithm is proposed to predict the blast and non-blast cells of leukemia. This approach proceeds with three distinct phases: (i) improved dominance soft set-based attribute reduction using AND operation in multi-soft set theory, (ii) generation of decision rules using dominance soft set, and (iii) rule pruning. The efficiency of the proposed system is compared with other benchmark classification algorithms. The research outcomes demonstrate that the derived rules efficiently classify cancer and non-cancer cells. Classification metrics are applied along with receiver operating characteristic (ROC) curve analysis to evaluate the efficiency of the proposed framework. Full article

(This article belongs to the Special Issue Machine Learning Applied to Medical Image Analysis)

► Show Figures

Figure 1

Review

Jump to: Research

29 pages, 16101 KiB

Open AccessReview

A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

by Kyriakos D. Apostolidis and George A. Papakostas

Electronics 2021, 10(17), 2132; https://doi.org/10.3390/electronics10172132 - 02 Sep 2021

Cited by 35 | Viewed by 6015

Abstract

In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to face numerous challenging problems. One of the most important challenges in the CV area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper function of these models can significantly upgrade the health systems. However, recent studies have shown that CNN models are vulnerable under adversarial attacks with imperceptible perturbations. In this paper, we summarize existing methods for adversarial attacks, detections and defenses on medical imaging. Finally, we show that many attacks, which are undetectable by the human eye, can degrade the performance of the models, significantly. Nevertheless, some effective defense and attack detection methods keep the models safe to an extent. We end with a discussion on the current state-of-the-art and future challenges. Full article

(This article belongs to the Special Issue Machine Learning Applied to Medical Image Analysis)

► Show Figures

Journal Menu

Journal Browser

Machine Learning Applied to Medical Image Analysis

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI