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Special Issue Editors

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has been increasingly used in medical imaging to improve diagnostic accuracy, reduce interpretation time, and enhance patient outcomes. AI algorithms can analyze medical images such as X-rays, CT scans, MRI scans, and ultrasound images, as well as provide valuable insights for clinicians. The main applications of AI in medical imaging are image analysis, image segmentation, and image registration.

The use of AI in medical imaging has the potential to improve patient outcomes by providing more accurate and timely diagnoses, guiding treatment decisions, and reducing the burden on clinicians. However, there are also challenges associated with implementing AI in clinical practice, such as ensuring the accuracy and reliability of the algorithms and addressing issues related to data privacy and security.

Machine learning and data-driven artificial intelligence methods are heavily conditioned on the available data. Authors should clearly state their data sources and the reasons for inclusion/exclusion of the recruited cases. Case selection should be clearly explained, as cherry picking has been shown as a source of bias. Limitations of the study and the application of methods beyond the specific data used for training should be stated in a separate section. Data should be offered in an open access format for potential rebuttal/confirmation by third-party researchers.

This Special Issue will explore, but is not restricted to, the following topics:

Machine learning algorithms for medical image analysis;
Use of AI in pathology and histology for image analysis;
Applications of AI in radiology and diagnostic imaging.

Prof. Dr. Manuel Graña
Prof. Dr. Carlos M. Chiesa-Estomba
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. Diagnostics 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 2600 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
machine learning
deep learning
medical imaging
X-ray
CT
MRI
ultrasound
diagnosis

Published Papers (2 papers)

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Research

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19 pages, 1117 KiB

Open AccessArticle

Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

by Bader Aldughayfiq, Farzeen Ashfaq, N. Z. Jhanjhi and Mamoona Humayun

Diagnostics 2023, 13(11), 1932; https://doi.org/10.3390/diagnostics13111932 - 01 Jun 2023

Cited by 10 | Viewed by 2872

Abstract

Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a “black box” that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model’s predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model’s predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Images: New Challenges and Future Perspectives)

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Review

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22 pages, 4018 KiB

Open AccessReview

Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

by Yuanqing Yang, Kai Sun, Yanhua Gao, Kuansong Wang and Gang Yu

Diagnostics 2023, 13(19), 3115; https://doi.org/10.3390/diagnostics13193115 - 03 Oct 2023

Cited by 4 | Viewed by 1914

Abstract

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP’s clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP. Full article

(This article belongs to the Special Issue Artificial Intelligence in Medical Images: New Challenges and Future Perspectives)

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