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Applied Sciences
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AI Technologies in Biomedical Image Processing and Analysis
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AI Technologies in Biomedical Image Processing and Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 4069

Special Issue Editors

Dr. Danila Germanese

E-Mail Website
Guest Editor
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), 56124 Pisa, Italy
Interests: biomedical devices; biomedical sensors; biomedical image analysis; artificial intelligence; radiomics; machine learning; deep learning
Dr. Maria Antonietta Pascali

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council of Italy (CNR), 56124 Pisa, Italy
Interests: geometry; modeling; computer vision
Dr. Lorenzo Faggioni

E-Mail Website
Guest Editor
Department of Translational Research, University of Pisa, 56126 Pisa, Italy
Interests: computed tomography; magnetic resonance imaging; contrast media; oncologic imaging; cardiac imaging; imaging informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging is developing rapidly thanks to enormous progress achieved in image processing and analysis. Such developments are largely due to AI: by simplifying data interpretation through sophisticated mathematical algorithms, AI has shown, in recent years, to be capable of assisting radiologists and clinicians in detecting diseases, assessing severity and prognosis, or automatically recognizing and quantifying disease features.

AI is becoming a valuable tool that, on one hand, if combined with the human expertise of radiologists and clinicians, may offer vast potential to the healthcare industry. On the other hand, MDs wants to have insight into how the algorithms or outcomes are calculated instead of just seeing an AI black box.

This Special Issue will focus on AI-based solutions and technologies developed to meet the challenges of biomedical image processing and analysis. It will identify innovative AI-based approaches for disease detection, diagnosis, severity assessment and prognosis prediction.

In addition, it will highlight the potential and the limitations of such AI-based solutions in improving the quality and the accuracy of medical image-based diagnosis in current and future applications.

The Special Issue will welcome submissions of original research articles, case studies, and critical reviews.

Dr. Danila Germanese
Dr. Maria Antonietta Pascali
Dr. Lorenzo Faggioni
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

  • biomedical image analysis
  • biomedical image processing
  • medical imaging
  • artificial intelligence
  • machine learning
  • deep learning
  • AI technologies

Published Papers (3 papers)

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Research

24 pages, 13431 KiB  
Article
Toward Lightweight Diabetic Retinopathy Classification: A Knowledge Distillation Approach for Resource-Constrained Settings
by Niful Islam, Md. Mehedi Hasan Jony, Emam Hasan, Sunny Sutradhar, Atikur Rahman and Md. Motaharul Islam
Appl. Sci. 2023, 13(22), 12397; https://doi.org/10.3390/app132212397 - 16 Nov 2023
Viewed by 1243
Abstract
Diabetic retinopathy (DR), a consequence of diabetes, is one of the prominent contributors to blindness. Effective intervention necessitates accurate classification of DR; this is a need that computer vision-based technologies address. However, using large-scale deep learning models for DR classification presents difficulties, especially [...] Read more.
Diabetic retinopathy (DR), a consequence of diabetes, is one of the prominent contributors to blindness. Effective intervention necessitates accurate classification of DR; this is a need that computer vision-based technologies address. However, using large-scale deep learning models for DR classification presents difficulties, especially when integrating them into devices with limited resources, particularly in places with poor technological infrastructure. In order to address this, our research presents a knowledge distillation-based approach, where we train a fusion model, composed of ResNet152V2 and Swin Transformer, as the teacher model. The knowledge learned from the heavy teacher model is transferred to the lightweight student model of 102 megabytes, which consists of Xception with a customized convolutional block attention module (CBAM). The system also integrates a four-stage image enhancement technique to improve the image quality. We compared the model against eight state-of-the-art classifiers on five evaluation metrics; the experiments show superior performance of the model over other methods on two datasets (APTOS and IDRiD). The model performed exceptionally well on the APTOS dataset, achieving 100% accuracy in binary classification and 99.04% accuracy in multi-class classification. On the IDRiD dataset, the results were 98.05% for binary classification accuracy and 94.17% for multi-class accuracy. The proposed approach shows promise for practical applications, enabling accessible DR assessment even in technologically underdeveloped environments. Full article
(This article belongs to the Special Issue AI Technologies in Biomedical Image Processing and Analysis)
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14 pages, 8913 KiB  
Article
Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models
by Hisaichi Shibata, Shouhei Hanaoka, Yang Cao, Masatoshi Yoshikawa, Tomomi Takenaga, Yukihiro Nomura, Naoto Hayashi and Osamu Abe
Appl. Sci. 2023, 13(18), 10132; https://doi.org/10.3390/app131810132 - 08 Sep 2023
Cited by 1 | Viewed by 997
Abstract
Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical [...] Read more.
Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve ϵ-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The ϵ-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI. Full article
(This article belongs to the Special Issue AI Technologies in Biomedical Image Processing and Analysis)
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13 pages, 3511 KiB  
Article
Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project
by Arnaud Berenbaum, Hervé Delingette, Aurélien Maire, Cécile Poret, Claire Hassen-Khodja, Stéphane Bréant, Christel Daniel, Patricia Martel, Lamiae Grimaldi, Marie Frank, Emmanuel Durand and Florent L. Besson
Appl. Sci. 2023, 13(9), 5281; https://doi.org/10.3390/app13095281 - 23 Apr 2023
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Abstract
Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures [...] Read more.
Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures and data processing workflows were specifically developed within this infrastructure and applied retrospectively to a monocentric dataset as a proof of concept. A custom-made 3D-CNN was first trained and tested on an “unambiguous” well-balanced data sample, which included strictly normal and highly pathological scans. For the training phase, 90% of the data sample was used (learning set: 80%; validation set: 20%, 5-fold cross validation) and the remaining 10% constituted the test set. Finally, the model was applied to a “real-life” test set which included any scans taken. Text mining of the PET reports systematically combined with visual rechecking by an experienced reader served as the standard-of-truth for PET labeling. Results: From 8125 scans, 4963 PETs had processable cross-matched medical reports. For the “unambiguous” dataset (1084 PETs), the 3D-CNN’s overall results for sensitivity, specificity, positive and negative predictive values and likelihood ratios were 84%, 98%, 98%, 85%, 42.0 and 0.16, respectively (F1 score of 90%). When applied to the “real-life” dataset (4963 PETs), the sensitivity, NPV, LR+, LR− and F1 score substantially decreased (61%, 40%, 2.97, 0.49 and 73%, respectively), whereas the specificity and PPV remained high (79% and 90%). Conclusion: An AI-based triage of whole-body FDG PET is promising. Further studies are needed to overcome the challenges presented by the imperfection of real-life PET data. Full article
(This article belongs to the Special Issue AI Technologies in Biomedical Image Processing and Analysis)
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