Special Issue "Machine Learning in Healthcare and Biomedical Application"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 1 December 2020.

Special Issue Editor

Dr. Alessia Sarica
Website
Guest Editor
Neuroscience Research Center, Department of Medical and Surgical Sciences, “Magna Graecia” University, Italy
Interests: machine learning; decision trees; ensemble learning; computer-aided diagnosis; neurosciences; neuroimaging

Special Issue Information

Dear Colleagues,

I invite you to submit original scientific contributions on the topic “Machine Learning in Healthcare and Biomedical Application”. The huge advances in Machine Learning (ML) have increased the opportunity to improve and speed up clinical decisions in numerous biomedical fields. The present Special Issue focuses on the revolutionary changes to medicine brought about by ML. In particular, we aim to present the most recent discoveries on the application of new or state-of-the-art ML algorithms (e.g., supervised and unsupervised learning; feature selection, extraction and reduction; ensemble learning; deep learning; interpretability and explainability of ML models) in areas related, but not limited to

  • Disease identification, differential diagnosis, and prognosis;
  • Bioimage processing and analysis;
  • Emotion recognition in healthcare;
  • Cognitive and psychological profiling;
  • Epidemic outbreak prediction;
  • Personalized medicine.

Contributions such as systematic reviews or meta-analyses on the above-mentioned topics are also welcome.

Dr. Alessia Sarica
Guest Editor

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. Algorithms 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 1000 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 for healthcare
  • computer-aided diagnosis
  • automatic clinical decision
  • epidemic outbreak prediction algorithms
  • personalized medicine

Published Papers (1 paper)

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Research

Open AccessArticle
Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach
Algorithms 2020, 13(10), 263; https://doi.org/10.3390/a13100263 - 15 Oct 2020
Abstract
In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after [...] Read more.
In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application)
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