Special Issue "Deep Learning for Medical Images: Challenges and Solutions"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editor

Prof. Dr. Jitae Shin
Guest Editor
School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 440-746, Korea
Interests: deep learning; image/video signal processing

Special Issue Information

Dear Colleagues,

Recently, with the great success of artificial intelligence (AI) and deep learning (DL) in areas of natural images, adapting and further developing DL techniques to medical images is an important and relevant research challenge. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics, and healthcare in general. Medical images achieved by modalities including X-rays, magnetic resonance, microwaves, ultrasound, and optical methods have been used widely for clinical purposes. It is almost impossible for clinicians to diagnose diseases without the aid of medical images. However, interpreting these images manually is time-consuming, expensive, and varies depending on the clinician’s expertise. Advances in DL enable us to automatically extract more information from images more confidently than ever before. The techniques of AI and DL have played an important role in medical fields like medical image processing, computer-aided diagnosis, image interpretation, image fusion, image registration, image segmentation, etc.

This Special Issue calls for papers presenting novel works about medical image/video processing using DL and AI. Furthermore, high-quality review and survey papers are welcomed. The papers considered for possible publication may focus on, but not necessarily be limited to, the following areas:

  • Deep learning and artificial intelligence for medical image/video;
  • Image segmentation, registration, and fusion;
  • Image processing and analysis;
  • Image formation/reconstruction and image quality assessment;
  • Medical image analysis;
  • Computer aided diagnosis;
  • Machine learning of big data in imaging;
  • Integration of imaging with non-imaging biomarkers;
  • Visualization in medical imaging.

Prof. Dr. Jitae Shin
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. 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.

Published Papers (1 paper)

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Open AccessArticle
Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model
Electronics 2020, 9(3), 445; https://doi.org/10.3390/electronics9030445 - 06 Mar 2020
Cited by 1
Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. [...] Read more.
Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset. Full article
(This article belongs to the Special Issue Deep Learning for Medical Images: Challenges and Solutions)
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