Machine Learning for Medical Image Analysis

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 5466

Special Issue Editors

School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: medical image analysis; multimodal brain image analysis; image segmentation; computer-aided detection (CAD); computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
Interests: Signal and information processing; machine learning with applications to image, video, communications and security
Department of Eye and Vision Science, University of Liverpool, Liverpool L3 5TR, UK
Interests: medical imaging; artificial intelligence; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With advances in medical imaging, there is an increasing demand for robust algorithms to intelligently and efficiently exploit the breadth of available visual information for better interpretation of medical images. It is rather challenging to derive analytic solutions to represent objects such as organs or lesions due to large variations in intensity, shape, and location of the lesions within areas of complicated anatomies in the images. To this end, machine learning has been successfully employed in many medical image applications including organ/lesion segmentation, tissue classification, computer-aided diagnosis, image-guided therapy, and image registration and fusion. The goal of this Special Issue is to publish the latest research advancements that integrate machine learning for the analysis of medical images. Topics of this Special Issue include (but are not limited to) machine learning methods with their applications in:

• Medical and biomedical image segmentation
• Classification and analysis of anatomical structures, lesions and lesion stages
• Computer-aided detection/diagnosis
• Multiple modality fusion
• Medical image reconstruction
• Pathology image analysis
• Retinal image analysis

Machine learning methods include (but are not limited to):

• Deep neural networks
• Generative adversarial networks
• Artificial neural networks
• Random forest
• Support vector machines
• Bayesian methods
• Manifold learning

Dr. Xujiong Ye
Dr. Victor Sanchez
Dr. Yalin Zheng
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. 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 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

  • Medical image segmentation
  • Computer-aided detection/diagnosis
  • Medical/biomedical image analysis
  • Multiple modalities
  • Artificial Intelligence
  • Deep neural networks
  • Manifold learning
  • Statistical pattern recognition

Published Papers (1 paper)

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Research

12 pages, 2676 KiB  
Article
Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images
by Qingge Ji, Wenjie He, Jie Huang and Yankui Sun
Algorithms 2018, 11(6), 88; https://doi.org/10.3390/a11060088 - 20 Jun 2018
Cited by 21 | Viewed by 4886
Abstract
We present an automatic method based on transfer learning for the identification of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retinal optical coherence tomography (OCT) images. The algorithm aims to improve the classification performance of retinal OCT images and [...] Read more.
We present an automatic method based on transfer learning for the identification of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retinal optical coherence tomography (OCT) images. The algorithm aims to improve the classification performance of retinal OCT images and shorten the training time. Firstly, we remove the last several layers from the pre-trained Inception V3 model and regard the remaining part as a fixed feature extractor. Then, the features are used as input of a convolutional neural network (CNN) designed to learn the feature space shifts. The experimental results on two different retinal OCT images datasets demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Machine Learning for Medical Image Analysis)
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