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Algorithms
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Machine Learning Algorithms for Biomedical Image Analysis and Applications
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Machine Learning Algorithms for Biomedical Image Analysis and Applications

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 (28 February 2025) | Viewed by 7142

Special Issue Editors

Dr. Ines Prata Machado

E-Mail Website
Guest Editor
Department of Oncology, University of Cambridge, Cambridge CB2 1TN, UK
Interests: clinical data analytics; healthcare decision support systems
Dr. Francesco Prinzi

E-Mail Website
Guest Editor
1. Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
2. Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
Interests: medical image analysis; radiomics; machine learning; explainable AI; multimodal learning
Dr. Carmelo Militello

E-Mail Website
Guest Editor
Institute for High-Performance Computing and Networking (ICAR-CNR), National Research Council, 90146 Palermo, Italy
Interests: medical images analysis and quantification; radiomics; applied machine learning, explainable predictive models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the infusion of architectural and algorithmic innovations within the realm of machine learning has revolutionized medical image analysis. Despite these advancements, the assimilation of these models into clinical practice bears several challenges. The wide availability and heterogeneity of open data offer the opportunity to train increasingly ambitious models, but which harbor a myriad of pitfalls.

Such challenges include the need for multimodal training, data harmonization, training small dataset scenarios, etc. Additionally, stringent requirements for explainability and reliability imposed by regulatory agencies add further complexity to the integration of machine learning models in clinical settings. Addressing these challenges holds immense potential for transformative impacts on healthcare, particularly in advancing the concepts of precision and personalized medicine.

This Special Issue will provide a forum to publish original research papers covering state-of-the-art and novel algorithms, methodologies, and applications of computational methods for biomedical image analysis and quantification, as well as to implement predictive models for precision medicine and clinical decision support systems.

Topics of interest include, but are not limited to, the following:

  • Biomedical image analysis algorithms and applications;
  • Machine learning and deep learning methods for medical image analysis;
  • Multimodal learning;
  • Data integration;
  • Data harmonization;
  • Radiomics;
  • Explainable AI techniques for interpretable and transparent AI.

Dr. Ines Prata Machado
Dr. Francesco Prinzi
Dr. Carmelo Militello
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

  • biomedical imaging
  • precision medicine
  • health informatics
  • radiomics
  • machine-learning
  • deep-learning
  • clinical decision support systems
  • explainable artificial intelligence

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Published Papers (5 papers)

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Research

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23 pages, 89226 KiB  
Article
Improving Vertebral Fracture Detection in C-Spine CT Images Using Bayesian Probability-Based Ensemble Learning
by Abhishek Kumar Pandey, Kedarnath Senapati, Ioannis K. Argyros and G. P. Pateel
Algorithms 2025, 18(4), 181; https://doi.org/10.3390/a18040181 - 21 Mar 2025
Viewed by 349
Abstract
Vertebral fracture (VF) may induce spinal cord injury that can lead to serious consequences which eventually may paralyze the entire or some parts of the body depending on the location and severity of the injury. Diagnosis of VFs is crucial at the initial [...] Read more.
Vertebral fracture (VF) may induce spinal cord injury that can lead to serious consequences which eventually may paralyze the entire or some parts of the body depending on the location and severity of the injury. Diagnosis of VFs is crucial at the initial stage, which may be challenging because of the subtle features, noise, and homogeneity present in the computed tomography (CT) images. In this study, Wide ResNet-40, DenseNet-121, and EfficientNet-B7 are chosen, fine-tuned, and used as base models, and a Bayesian-based probabilistic ensemble learning method is proposed for fracture detection in cervical spine CT images. The proposed method considers the prediction’s uncertainty of the base models and combines the predictions obtained from them, to improve the overall performance significantly. This method assigns weights to the base learners, based on their performance and confidence about the prediction. To increase the robustness of the proposed model, custom data augmentation techniques are performed in the preprocessing step. This work utilizes 15,123 CT images from the RSNA-2022 C-spine fracture detection challenge and demonstrates superior performance compared to the individual base learners, and the other existing conventional ensemble methods. The proposed model also outperforms the best state-of-the-art (SOTA) model by 1.62%, 0.51%, and 1.29% in terms of accuracy, specificity, and sensitivity, respectively; furthermore, the AUC score of the best SOTA model is lagging by 5%. The overall accuracy, specificity, sensitivity, and F1-score of the proposed model are 94.62%, 93.51%, 95.29%, and 93.16%, respectively. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Image Analysis and Applications)
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18 pages, 3112 KiB  
Article
Development and External Validation of [18F]FDG PET-CT-Derived Radiomic Models for Prediction of Abdominal Aortic Aneurysm Growth Rate
by Simran Singh Dhesi, Pratik Adusumilli, Nishant Ravikumar, Mohammed A. Waduud, Russell Frood, Alejandro F. Frangi, Garry McDermott, James H. F. Rudd, Yuan Huang, Jonathan R. Boyle, Maysoon Elkhawad, David E. Newby, Nikhil Joshi, Jing Yi Kwan, Patrick Coughlin, Marc A. Bailey and Andrew F. Scarsbrook
Algorithms 2025, 18(2), 86; https://doi.org/10.3390/a18020086 - 5 Feb 2025
Viewed by 766
Abstract
Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. [...] Read more.
Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. RFs were extracted from manual segmentations of AAAs using PyRadiomics. Recursive feature elimination (RFE) reduced features for model optimisation. A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Accuracy was evaluated via cross-validation, with uncertainty quantified using dropout (MLP), standard deviation (RF), and 95% prediction intervals (XGBoost). External validation used independent data from two centres. Ground truth growth rates were calculated from serial ultrasound (US) measurements or CT volumes. Results (3): From 93 initial RFs, 29 remained after RFE. The MLP model achieved an MAE ± SEM of 1.35 ± 3.2e−4 mm/year with the full feature set and 1.35 ± 2.5e−4 mm/year with RFE. External validation yielded 1.8 ± 8.9e−8 mm/year. RF, XGBoost, and SVM models produced comparable accuracies internally (1.4–1.5 mm/year) but showed higher errors during external validation (1.9–1.97 mm/year). The MLP model demonstrated reduced uncertainty with the full feature set across all datasets. Conclusions (4): An MLP model leveraging [18F]FDG PET-CT radiomics accurately predicted AAA growth rates and generalised well to external data. In the future, more sophisticated stratification could guide individualised patient care, facilitating risk-tailored management of AAAs. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Image Analysis and Applications)
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29 pages, 8768 KiB  
Article
HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings
by Syed Atif Moqurrab, Hari Mohan Rai and Joon Yoo
Algorithms 2024, 17(8), 364; https://doi.org/10.3390/a17080364 - 19 Aug 2024
Cited by 4 | Viewed by 1093
Abstract
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, [...] Read more.
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Image Analysis and Applications)
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20 pages, 7925 KiB  
Article
Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
by Lei Zhang, Xiaoke Wang, Michael Rawson, Radu Balan, Edward H. Herskovits, Elias R. Melhem, Linda Chang, Ze Wang and Thomas Ernst
Algorithms 2024, 17(5), 215; https://doi.org/10.3390/a17050215 - 15 May 2024
Cited by 4 | Viewed by 2458
Abstract
Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have [...] Read more.
Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Image Analysis and Applications)
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Review

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33 pages, 437 KiB  
Review
The Diagnostic Classification of the Pathological Image Using Computer Vision
by Yasunari Matsuzaka and Ryu Yashiro
Algorithms 2025, 18(2), 96; https://doi.org/10.3390/a18020096 - 8 Feb 2025
Viewed by 1521
Abstract
Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster and more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), have shown superior performance in tasks such as image classification, segmentation, and object detection in [...] Read more.
Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster and more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), have shown superior performance in tasks such as image classification, segmentation, and object detection in pathology. Computer vision has significantly improved the accuracy of disease diagnosis in healthcare. By leveraging advanced algorithms and machine learning techniques, computer vision systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep learning models have been trained on large datasets of annotated pathology images to perform tasks such as cancer diagnosis, grading, and prognostication. While deep learning approaches show great promise in diagnostic classification, challenges remain, including issues related to model interpretability, reliability, and generalization across diverse patient populations and imaging settings. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Image Analysis and Applications)
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