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Algorithms
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AI Algorithms in Medical Imaging
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AI Algorithms in Medical Imaging

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 8415

Special Issue Editors

Dr. Yan Pei

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
Interests: computational intelligence; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianqiang Li

E-Mail Website
Guest Editor
School of Software Engineering, Beijing University of Technology, Beijing 100124, China
Interests: enterprise information system; data center optimization; data mining; privacy protection; big data analysis; cloud computing
Dr. Faheem Akhtar Rajput

E-Mail Website
Guest Editor
Computer Science Department, Sukkur IBA University, Sukkur 65200, Pakistan
Interests: bioinformatics; machine learning; deep learning; image recognition; computer vision

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research on AI algorithms in medical imaging. This Special Issue will provide a forum for disseminating new research results on medical and biological image analysis, and how AI is currently playing an active role in this field. The proposed Special Issue, “AI algorithms in medical imaging”, will publish original peer-reviewed research articles on the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage diseases. Furthermore, the prime focus of this Special Issue is on unifying the sciences of medicine, biology, and imaging, emphasizing the common ground where instrumentation, hardware, software, mathematics, physics, biology, and medicine interact through new analysis methods. Contributions are encouraged that describe novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and related methods. Studies involving highly technical perspectives are most welcome.

Dr. Yan Pei
Prof. Dr. Jianqiang Li
Dr. Faheem Akhtar Rajput
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. Algorithms 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 1600 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

  • medical images
  • machine learning
  • deep learning
  • feature extraction
  • bioinspired computing
  • artificial intelligence in healthcare
  • biomedical signal and image processing
  • pattern recognition
  • bioinformatics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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17 pages, 3911 KiB  
Article
Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception
by Ye Bian, Chengyong Si and Lei Wang
Algorithms 2024, 17(4), 164; https://doi.org/10.3390/a17040164 - 19 Apr 2024
Viewed by 1380
Abstract
The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus [...] Read more.
The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability. Full article
(This article belongs to the Special Issue AI Algorithms in Medical Imaging)
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17 pages, 4914 KiB  
Article
White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
by Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Algorithms 2023, 16(11), 525; https://doi.org/10.3390/a16110525 - 15 Nov 2023
Cited by 7 | Viewed by 3873
Abstract
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these [...] Read more.
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead. Full article
(This article belongs to the Special Issue AI Algorithms in Medical Imaging)
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Review

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12 pages, 473 KiB  
Review
The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools
by Gavino Faa, Massimo Castagnola, Luca Didaci, Fernando Coghe, Mario Scartozzi, Luca Saba and Matteo Fraschini
Algorithms 2024, 17(6), 254; https://doi.org/10.3390/a17060254 - 10 Jun 2024
Cited by 3 | Viewed by 1932
Abstract
The introduction of machine learning in digital pathology has deeply impacted the field, especially with the advent of whole slide image (WSI) analysis. In this review, we tried to elucidate the role of machine learning algorithms in diagnostic precision, efficiency, and the reproducibility [...] Read more.
The introduction of machine learning in digital pathology has deeply impacted the field, especially with the advent of whole slide image (WSI) analysis. In this review, we tried to elucidate the role of machine learning algorithms in diagnostic precision, efficiency, and the reproducibility of the results. First, we discuss some of the most used tools, including QuPath, HistoQC, and HistomicsTK, and provide an updated overview of machine learning approaches and their application in pathology. Later, we report how these tools may simplify the automation of WSI analyses, also reducing manual workload and inter-observer variability. A novel aspect of this review is its focus on open-source tools, presented in a way that may help the adoption process for pathologists. Furthermore, we highlight the major benefits of these technologies, with the aim of making this review a practical guide for clinicians seeking to implement machine learning-based solutions in their specific workflows. Moreover, this review also emphasizes some crucial limitations related to data quality and the interpretability of the models, giving insight into future directions for research. Overall, this work tries to bridge the gap between the more recent technological progress in computer science and traditional clinical practice, supporting a broader, yet smooth, adoption of machine learning approaches in digital pathology. Full article
(This article belongs to the Special Issue AI Algorithms in Medical Imaging)
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