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Integrating Generative AI with Medical Imaging

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Special Issue Editors

Dr. Shunxing Bao

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Guest Editor

Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37240, USA
Interests: medical image processing and big data; distributed computing; digital pathology; generative AI

Dr. Peng Zhang

E-Mail Website
Guest Editor

Department of Computer Science and Data Science Institute, Vanderbilt University, Nashville, TN 37240, USA
Interests: healthcare interoperability; healthcare informatics; data science; generative AI; transfer learning

Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of medical research and technological innovation, the integration of Generative Artificial Intelligence (AI) with medical imaging has emerged as a transformative approach. This Special Issue is dedicated to illuminating the diverse ways in which Generative AI is enhancing medical imaging, accommodating various imaging modalities, and fostering a bridge from single to multi-modality analyses.

The foundational focus of this Special Issue centers on an in-depth exploration of how Generative AI techniques are elevating the field of medical imaging to unprecedented levels of advancement. Aligned with the focus of our journal, this issue underscores our commitment to pushing the boundaries of medical research through innovative applications of AI. Our objective is to curate a collection of scholarly articles that underscore the synergy between Generative AI and various medical imaging modalities. This collection will not only highlight the myriad applications of Generative AI in enhancing medical image analysis but also emphasize the potential of integrating multi-modality imaging for a comprehensive understanding of complex medical conditions.

We invite your contributions to this Special Issue, where we aspire to cover a diverse range of themes, including but not limited to:

Innovations in Generative AI techniques for medical image synthesis and image enhancement;
Leveraging Generative AI for accurate segmentation and annotation of medical images;
Generative AI-powered disease classification, diagnosis, and prognosis from diverse medical imaging data;
Exploring the potential of multi-modality medical imaging analysis using Generative AI;
Ethical considerations and interpretability of Generative AI-driven medical image analysis;
Bridging the gap between Generative AI research and clinical applications in medical imaging.

We encourage original research articles, reviews, and case studies that highlight the potential of Generative AI in reshaping medical imaging analyses, promoting cross-modality collaborations, and advancing healthcare practices. We look forward to receiving your contributions.

Dr. Shunxing Bao
Dr. Peng Zhang
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. J is an international peer-reviewed open access quarterly 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 1200 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

generative AI
multi-modality medical imaging analysis
medical image segmentation and annotation
clinical research
AI ethics and interpretability

Published Papers (1 paper)

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Research

24 pages, 3065 KiB

Open AccessArticle

An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

by Maria Vasiliki Sanida, Theodora Sanida, Argyrios Sideris and Minas Dasygenis

J 2024, 7(1), 48-71; https://doi.org/10.3390/j7010003 - 22 Jan 2024

Cited by 1 | Viewed by 1415

Abstract

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases. Full article

(This article belongs to the Special Issue Integrating Generative AI with Medical Imaging)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques
Authors: Theodora Sanida
Affiliation: Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece
Abstract: Chest X-ray imaging is an essential and indispensable tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including accuracy, recall, F1-score, precision, and Area Under the Curve (AUC). This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector.