Computer-Aided Diagnosis for Biomedical Engineering

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1241

Special Issue Editors


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Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 205513, Brazil
Interests: intelligent systems; machine learning; embedded systems; swarm robotics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Interests: big data; bioinformatics; computational intelligence; data science; energy monitoring and management; intelligent transportation; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce the establishment of a Special Issue in Bioengineering, focusing on "Computer-Aided Diagnosis for Biomedical Engineering." As the field of biomedical engineering continues to progress, the integration of computer-aided diagnosis techniques has become increasingly indispensable in improving healthcare outcomes.

This Special Issue aims to gather a comprehensive collection of research papers that explore the latest advancements in computer-aided diagnosis within the realm of biomedical engineering. By bringing together experts in this field, we strive to promote interdisciplinary collaboration and knowledge exchange to enhance the accuracy, efficiency, and accessibility of medical diagnoses.

Topics of interest include, but are not limited to:

  • Machine learning and deep learning approaches in computer-aided diagnosis.
  • Virtual reality and augmented reality technologies for medical imaging and diagnostic support.
  • Secure Data Transmission and Storage in Computer-Aided Diagnosis Systems
  • Ethical and Legal Considerations in Computer-Aided Biomedical Diagnostics
  • Developing Robust and Explainable AI Models for Biomedical Applications
  • AI-Driven Early Detection and Prognosis of Chronic Diseases
  • Image processing and analysis techniques applied to biomedical diagnostics.
  • Novel algorithms and tools for computer-aided diagnosis in specific medical disciplines (e.g., cardiology, neurology, oncology, etc.).
  • Integration of artificial intelligence and computer vision in diagnosing medical images.
  • Innovative bioinformatics approaches for medical data interpretation and diagnosis.
  • Clinical decision support systems based on computer-aided diagnosis algorithms.

We invite researchers, engineers, and healthcare professionals to submit their original research papers, reviews, or case studies to this Special Issue. By disseminating cutting-edge research, we aim to foster advancements in computer-aided diagnosis, ultimately benefiting patients worldwide.

Dr. Brij Gupta
Dr. Nadia Nedjah
Dr. Kwok Tai Chui
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. 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

  • computer-aided diagnosis
  • biomedical engineering
  • secure biomedical
  • machine learning
  • deep learning
  • image processing
  • medical imaging
  • clinical decision support systems

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Published Papers (1 paper)

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Research

15 pages, 1791 KiB  
Article
NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
by Saul Fuster, Umay Kiraz, Trygve Eftestøl, Emiel A. M. Janssen and Kjersti Engan
Bioengineering 2024, 11(9), 909; https://doi.org/10.3390/bioengineering11090909 - 11 Sep 2024
Viewed by 809
Abstract
The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, although it suffers from inconsistencies and variations among pathologists. Moreover, absence [...] Read more.
The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, although it suffers from inconsistencies and variations among pathologists. Moreover, absence of annotations in medical imaging renders it difficult to train deep learning models. To address these challenges, we introduce a pipeline designed for bladder cancer grading using histological slides. First, it extracts urothelium tissue tiles at different magnification levels, employing a convolutional neural network for processing for feature extraction. Then, it engages in the slide-level prediction process. It employs a nested multiple-instance learning approach with attention to predict the grade. To distinguish different levels of malignancy within specific regions of the slide, we include the origins of the tiles in our analysis. The attention scores at region level are shown to correlate with verified high-grade regions, giving some explainability to the model. Clinical evaluations demonstrate that our model consistently outperforms previous state-of-the-art methods, achieving an F1 score of 0.85. Full article
(This article belongs to the Special Issue Computer-Aided Diagnosis for Biomedical Engineering)
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