Special Issue "Deep Learning in Medical Image Analysis, Volume II"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Prof. Dr. Juan Manuel Gorriz
E-Mail Website
Guest Editor
Department of Signal Theory, Telematics and Communications, University of Granada, Granada 18071, Spain
Interests: artificial intelligence; statistical signal processing; biomedical applications; machine learning
Special Issues and Collections in MDPI journals
Prof. Dr. Zhengchao Dong
E-Mail Website
Guest Editor
1. Molecular Imaging and Neuropathology Division, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA
2. New York State Psychiatric Institute, New York, NY 10032, USA
Interests: structural mechanics; computational mechanics; contact mechanics; efficient solvers; interfaces; modeling; applications in mechanical and civil engineering
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, deep learning has established itself as a powerful tool across a broad spectrum of domains in imaging, e.g., classification, prediction, detection, segmentation, diagnosis, interpretation, and reconstruction. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications.

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and in understanding the underlying biological process.

The purpose of this Special Issue on “Deep Learning in Medical Image Analysis, Volume II” is to present and highlight novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.

Prof. Dr. Yudong Zhang
Prof. Dr. Juan Manuel Gorriz
Prof. Dr. Zhengchao Dong
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. Journal of Imaging 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 1600 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

  • artificial intelligence
  • deep learning
  • transfer learning
  • deep neural network
  • convolutional neural network
  • graph neural network
  • multitask learning
  • explainable AI
  • attention mechanism
  • biomedical engineering
  • multimodal imaging
  • semantic segmentation
  • image reconstruction
  • healthcare

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?
J. Imaging 2021, 7(7), 105; https://doi.org/10.3390/jimaging7070105 - 25 Jun 2021
Viewed by 548
Abstract
The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular [...] Read more.
The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis, Volume II)
Show Figures

Figure 1

Back to TopTop