Special Issue "Computer-Aided Diagnosis and Characterization of Diseases"

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (30 September 2018).

Special Issue Editors

Prof. Luc Bidaut
E-Mail Website
Guest Editor
Professor of Medical Imaging, Head, School of Computer Science, College of Science, University of Lincoln, UK
Interests: advanced/quantitative/translational biomedical imaging and technologies, and all applications thereof
Dr. Kenji Suzuki
E-Mail Website
Guest Editor
Associate Professor, Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616, USA
Tel. +1 312 567 5232
Interests: deep learning, machine learning, computer-aided diagnosis, and medical imaging
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

While traditional diagnosis remains a key component of clinical workup, many diseases can nowadays benefit from earlier and more refined characterization in order to make the most of increasingly advanced - often personalized - therapies and multimodality treatment regimens. In the modern multidisciplinary clinical context, such advances tend to rely on the collation and analysis of much varied information and data obtained through at various stages of a patient's workup. The extraction, quantity and variety of such data and their subsequent synthesis is often well beyond human capabilities and thus requires the use of advanced computed analysis. This is where the concept of Computer-Aided Diagnosis and related approaches arose from and, while those were initially developed for extracting and combining features derived from images (e.g., from modalities such as X-ray, CT, mammography, MR, US, PET and SPECT), they have subsequently and more recently been broadened to encompass all types of clinical data and biomarkers (from genomics to imaging, alongside etiology, environment, etc.), with the aim of providing an actionable understanding of disease to assist with its detailed characterization (e.g., type, stage) and optimize interventional strategies. This Special Issue will gather as comprehensive as possible a collection of relevant examples of such approaches at any stage of the clinical workup, across specialties, disciplines and applications.

Prof. Luc Bidaut
Prof. Kenji Suzuki
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. Diagnostics is an international peer-reviewed open access quarterly 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

  • computer-aided detection and diagnosis

  • early detection, diagnosis, characterization and intervention

  • advanced imaging

  • quantitative imaging

  • image-guided therapy

  • personalized medicine

  • precision medicine

  • biomarkers

  • machine/deep learning

  • radiogenomics

  • radiomics

  • computational/artificial intelligence

Published Papers (2 papers)

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Research

Open AccessArticle
Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
Diagnostics 2018, 8(3), 56; https://doi.org/10.3390/diagnostics8030056 - 28 Aug 2018
Cited by 1
Abstract
Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more [...] Read more.
Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Characterization of Diseases)
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Open AccessArticle
Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
Diagnostics 2018, 8(3), 48; https://doi.org/10.3390/diagnostics8030048 - 25 Jul 2018
Cited by 2
Abstract
It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network [...] Read more.
It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis and Characterization of Diseases)
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