Special Issue "Machine Learning Applied to Medical Image Analysis"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Dr. Silvia Francesca Storti
Website
Guest Editor
Department of Computer Science, University of Verona, 37134 Verona, Italy
Interests: artificial intelligence; machine learning; multimodal functional neuroimaging integration; brain connectivity inference and network analysis; brain–computer interface
Dr. Francesco Setti
Website
Guest Editor
Department of Computer Science, University of Verona, 37134 Verona, Italy
Interests: machine learning; artificial intelligence; computer vision; human-robot interaction; multimodal learning; cognitive robotics

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 papers will be 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 monthly 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 1500 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 (1 paper)

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Research

Open AccessArticle
Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification
Electronics 2020, 9(5), 794; https://doi.org/10.3390/electronics9050794 - 12 May 2020
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 [...] Read more.
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)
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