Artificial Intelligence for Computer-Aided Detection in Biomedical Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2391

Special Issue Editor


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Guest Editor
Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
Interests: bioinformatics; imaging informatics; clinical decision support

Special Issue Information

Dear Colleagues,

The use of Artificial Intelligence (AI) in Computer-Aided Detection (CAD) has led to significant advancements in biomedical applications. AI encompasses the development of intelligent machines that can simulate human intelligence, enabling them to learn from large datasets and make predictions or decisions based on complex patterns and algorithms. CAD systems, on the other hand, aid healthcare professionals in the identification and analysis of various medical conditions, utilizing computer algorithms to improve accuracy and efficiency.

This Special Issue explores the integration of AI techniques within CAD systems to revolutionize biomedical applications. It aims to present work from researchers and practitioners from multidisciplinary backgrounds and discuss the latest advancements, challenges, and future prospects in this rapidly growing field.

Topics of interest within this Special Issue include, but are not limited to, the development and evaluation of novel AI algorithms for CAD in biomedical imaging, the application of machine learning techniques to enhance detection and diagnosis accuracy, the utilization of deep learning architectures in CAD systems, the integration of AI technologies into medical decision making, the impact of AI on CAD-assisted diagnosis and treatment planning, and ethical considerations surrounding the use of AI in biomedical applications.

The papers contributed to this Special Issue will provide valuable insights into the potential of AI-powered CAD systems in biomedical domains, paving the way for improved detection, diagnosis, prognosis, and personalized treatment strategies. Researchers and practitioners across fields such as computer science, biomedical engineering, radiology, medical imaging, and bioinformatics are encouraged to submit their original research, reviews, and case studies.

Dr. Lawrence Chan
Guest Editor

Manuscript Submission Information

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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. Bioengineering 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 2700 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
  • computer-aided detection
  • CAD
  • biomedical applications
  • biomedical engineering
  • radiology
  • medical imaging
  • bioinformatics
  • deep learning
  • machine learning
  • disease diagnosis

Published Papers (1 paper)

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Research

15 pages, 4435 KiB  
Article
Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation
by Luoyi Kong, Mohan Huang, Lingfeng Zhang and Lawrence Wing Chi Chan
Bioengineering 2024, 11(3), 270; https://doi.org/10.3390/bioengineering11030270 - 09 Mar 2024
Viewed by 1511
Abstract
Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize [...] Read more.
Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm. Full article
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