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Applied Sciences
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Computer-Assisted Image Analysis in Biomedicine
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Computer-Assisted Image Analysis in Biomedicine

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 3150

Special Issue Editors

Dr. Qingyu Zhao

E-Mail Website
Guest Editor
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
Interests: machine learning; image processing; neuroscience
Dr. Daniel Moyer

E-Mail Website
Guest Editor
Department of Computer Science, Vanderbilt University, Nashville, TN 37240, USA
Interests: medical imaging; machine learning
Dr. Magdalini Paschali

E-Mail Website
Guest Editor
Computational Neuroimage Science Laboratory (CNS Lab), Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
Interests: deep learning; computer vision; medical imaging; robustness

Special Issue Information

Dear Colleagues,

The discipline of medical image computing has created a broad impact on medicine and bioscience, facilitating a wide range of clinical applications related to diagnosis workflow, biomarker discovery, therapy, and surgery. These diverse applications are coupled with various distinct medical imaging modalities, requiring the development of various computational and analytical methods. This Special Issue aims to highlight advances in the areas of medical image computing and computer-assisted biomedical imaging applications. Topics of interest include, but are not limited to, computer-aided diagnosis, computational pathology and anatomy, medical image processing, image-guided interventions and surgery, population imaging for biomarker discovery, machine learning and statistical methods for medical imaging applications, combination between imaging and non-imaging data, and visualization of biomedical images. Article types can include original research, review, hypothesis and theory, perspective, data and case report, brief research report, technology, and code.

Dr. Qingyu Zhao
Dr. Daniel Moyer
Dr. Magdalini Paschali
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 analysis
  • machine learning
  • computer-aided diagnosis
  • population analysis
  • healthcare
  • medical imaging
  • bioinformatics
  • biostatistics

Published Papers (2 papers)

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Research

12 pages, 2479 KiB  
Article
Chromosome Cluster Type Identification Using a Swin Transformer
by Indu Joshi, Arnab Kumar Mondal and Nassir Navab
Appl. Sci. 2023, 13(14), 8007; https://doi.org/10.3390/app13148007 - 8 Jul 2023
Cited by 3 | Viewed by 1085
Abstract
The analysis of chromosome karyotypes is crucial for diagnosing genetic disorders such as Patau syndrome, Edward syndrome, and Down syndrome. Chromosome cluster type identification is a key step in the automated analysis of chromosome karyotypes. State-of-the-art chromosome cluster-type identification techniques are based on [...] Read more.
The analysis of chromosome karyotypes is crucial for diagnosing genetic disorders such as Patau syndrome, Edward syndrome, and Down syndrome. Chromosome cluster type identification is a key step in the automated analysis of chromosome karyotypes. State-of-the-art chromosome cluster-type identification techniques are based on convolutional neural networks (CNNs) and fail to exploit the global context. To address this limitation of the state of the art, this paper proposes a transformer network, chromosome cluster transformer (CCT), that exploits a swin transformer backbone and successfully captures long-range dependencies in a chromosome image. Additionally, we find that the proposed CCT has a large number of model parameters, which makes it prone to overfitting on a (small) dataset of chromosome images. To alleviate the limited availability of training data, the proposed CCT also utilizes a transfer learning approach. Experiments demonstrate that the proposed CCT outperforms the state-of-the-art chromosome cluster type identification methods as well as the traditional vision transformer. Furthermore, to provide insights on the improved performance, we demonstrate the activation maps obtained using Gradient Attention Rollout. Full article
(This article belongs to the Special Issue Computer-Assisted Image Analysis in Biomedicine)
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11 pages, 765 KiB  
Article
Respiratory Sound Classification by Applying Deep Neural Network with a Blocking Variable
by Runze Yang, Kexin Lv, Yizhang Huang, Mingxia Sun, Jianxun Li and Jie Yang
Appl. Sci. 2023, 13(12), 6956; https://doi.org/10.3390/app13126956 - 8 Jun 2023
Cited by 2 | Viewed by 1643
Abstract
Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten people’s lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence [...] Read more.
Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten people’s lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence of a doctor. Thus, it is worthwhile to develop an automatic respiratory disease detection system. This paper proposes a deep neural network with a blocking variable, namely Blnet, to classify respiratory sound, which integrates the strength of the ResNet, GoogleNet, and the self-attention mechanism. To solve the non-IID data problem, a two-stage training process with the blocking variable was developed. In addition, the mix-up data augmentation within the clusters was used to address the imbalanced data problem. The performance of the Blnet was tested on the ICBHI 2017 data, and the model achieved 79.13% specificity and 66.31% sensitivity, with an average score of 72.72%, which is a 4.22% improvement in the average score and a 12.61% improvement in sensitivity over the state-of-the-art results. Full article
(This article belongs to the Special Issue Computer-Assisted Image Analysis in Biomedicine)
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