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A Special Issue of Algorithms (ISSN 1999-4893) belonging to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 4844

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Dr. Lei Fan

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Centre for Healthy Brain Ageing (CHeBA), UNSW Sydney, Sydney, NSW 2052, Australia
Interests: cancer analysis (histopathology WSIs, multimodal); MRI data analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has become a transformative force in biomedical image analysis, offering unprecedented opportunities to enhance disease diagnosis, prognosis, and treatment planning. With the rapid evolution of deep learning, AI-driven approaches have demonstrated superior performance in extracting clinically relevant patterns from complex imaging data, highlighting their promise for future precision medicine.

Biomedical imaging encompasses diverse modalities, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), ultrasound, and whole-slide pathology images. Each modality captures unique biological and structural information, enabling tasks such as lesion detection, organ segmentation, disease classification, image registration, and quantitative biomarker extraction. AI not only accelerates these processes but also improves reproducibility and reduces observer variability, thereby facilitating more reliable clinical decision-making.

Recent advancements are driving the next wave of innovation. Self-supervised learning enables effective model training with limited annotations, while foundation models trained on massive datasets provide strong generalization across imaging modalities and clinical tasks. Large language models (LLMs) and vision–language models (VLMs) are opening new opportunities for medical image captioning, report generation, and cross-modal reasoning, bridging the gap between imaging data and clinical narratives. Multimodal learning further integrates imaging with genomics, electronic health records, and other biomedical signals, enabling a holistic understanding of disease processes. Together, these approaches are pushing biomedical image analysis toward more robust, scalable, and clinically meaningful solutions.

This Special Issue aims to showcase cutting-edge research on AI-powered biomedical image analysis, spanning novel algorithms, innovative applications, and translational studies bridging the gap between computational advances and clinical practice. By bringing together interdisciplinary contributions, the issue seeks to highlight both technical innovations and their implications for real-world healthcare.

Dr. Lei Fan
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.

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Keywords

biomedical image analysis
artificial intelligence
medical imaging data
CT/MRI
whole slide image

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Published Papers (6 papers)

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Research

18 pages, 3796 KB

Open AccessArticle

An Explainable Multimodal Deep Learning Framework for Glaucoma Detection and Progression Prediction Using Optical Coherence Tomography and Visual Field Data

by Ali Al-Ataby, Hussain Attia and Waleed Al-Nuaimy

Algorithms 2026, 19(8), 608; https://doi.org/10.3390/a19080608 - 23 Jul 2026

Viewed by 559

Abstract

Glaucoma is a leading cause of irreversible blindness, and timely detection and monitoring are essential to prevent permanent visual loss. Artificial intelligence (AI) has shown strong potential for automated diagnosis, but progression prediction remains a more challenging and clinically significant task. This study proposes an explainable multimodal deep learning framework for both glaucoma detection and progression prediction using the Harvard Glaucoma Detection and Progression dataset. The framework integrates specific modality encoders for optical coherence tomography B-scans, retinal nerve fiber layer thickness (RNFLT) maps, and visual field (VF) features through a feature fusion module. For the glaucoma detection task, the multimodal model achieved near-perfect performance with an AUROC of 0.9989 and a sensitivity of 0.9944. For the more complex progression prediction task, the full-fusion model achieved an AUROC of 0.7836. Using an optimized validation threshold, the model provided a balanced clinical operating point with reasonable sensitivity and specificity. Ablation experiments revealed a complementary relationship between modalities; VF features provided the strongest standalone ranking signal, RNFLT maps contributed the highest specificity, and multimodal fusion yielded the best overall precision–recall performance, although not the highest AUROC. Gradient-weighted Class Activation Mapping analysis indicated that the model focused on clinically plausible localized retinal regions. These findings support explainable and multimodal AI for glaucoma progression-risk identification. Full article

(This article belongs to the Special Issue AI-Powered Biomedical Image Analysis)

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28 pages, 3422 KB

Open AccessArticle

Towards Explainable and Robust Cervical Cancer Screening Using Domain-Specific Transfer Learning Algorithm

by Jheelam Mondal, Mahendra Kumar Gourisaria, Rajdeep Chatterjee, Amitkumar V. Jha, Bhargav Appasani, Nicu Bizon and Cristian Toma

Algorithms 2026, 19(7), 584; https://doi.org/10.3390/a19070584 - 16 Jul 2026

Viewed by 494

Abstract

Cervical cancer is the fourth most frequent malignancy in women globally. Pap smear screening is important for early cancer detection, but manual smear analysis is time-consuming, labor-intensive and error-prone for diagnosis. Such issues in resource-limited areas have led to the introduction of deep learning (DL) methods for automated cervical cancer diagnosis. But the majority of current methodologies depend on models pretrained on natural image datasets like ImageNet, which may inadequately represent domain-specific pathological characteristics. To mitigate this constraint, this research employs a domain-specific transfer learning algorithm approach using the PathMNIST histopathological dataset to enhance cervical cell classification. An accuracy score of 96.77% is achieved for the proposed YOLO* model, using the SIPAKMED dataset. To the best of available knowledge, no previous study has reported the use of PathMNIST as a pretraining source for cytology image classification. As domain-specific medical pretraining is becoming more popular, our study shows the importance of cross-domain generalization. Full article

(This article belongs to the Special Issue AI-Powered Biomedical Image Analysis)

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15 pages, 5143 KB

Open AccessArticle

Localization in Medical Imaging: A Unified AI Approach for Ovaries, Follicles, and Vertebral Arteries

by Andrey Moshkin, Maxim Fedorov, Vladimir Arlazarov, Valeria Gribova, Anton Nazarenko, Dmitry Repin, Olga Klevtsova and Aleksandr Romanov

Algorithms 2026, 19(7), 523; https://doi.org/10.3390/a19070523 - 29 Jun 2026

Cited by 1 | Viewed by 434

Abstract

Artificial intelligence (AI) technologies, which are being actively developed in modern medicine today, increase the speed and quality of patient care. This article mainly seeks to demonstrate the use of various options of computer analysis of clinical images to solve practical problems of increasing the efficiency of routine diagnostics using retrospective analysis, as well as show the potential for its widespread implementation (due to the scalability of the architecture) in practical healthcare, exemplified by ultrasound (US) and magnetic resonance imaging (MRI) data analysis. This is an interuniversity study, its research protocol was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the local Ethics Committee of Orel State University named after I. S. Turgenev (Protocol No. 25 dated 16 November 2022). The software was developed using Python 3.7 and open neural network models. Statistical processing included an efficiency assessment for which IBM SPSS Statistics 20.0 was used. Detection errors in the analysis of 550 US cases did not exceed 6–8% and were associated with technical difficulties due to image quality. When studying 1030 MRI studies, only 0.19% of cases failed to obtain reliable image analysis results. The differences in the average values for the dimensional characteristics of the studied vessels were 0.11–0.12 mm. The effectiveness of AI in clinical tasks is presented. The improvement in segmentation accuracy was achieved through the use of step-by-step image optimization during the AI training stage. The evolution of technologies in medicine, aimed at digitalization and personalization, is intended to improve the quality and speed of studying images in practical work. Full article

(This article belongs to the Special Issue AI-Powered Biomedical Image Analysis)

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24 pages, 16640 KB

Open AccessArticle

Surrogate-Based Optimization of Interpretable Regular Expression Patterns for the Classification of Retinal Lesions in Retinal Fundus Images

by Rafael A. García-Ramírez, Ivan Cruz-Aceves, Arturo Hernández-Aguirre, Juan-Manuel Lopez-Hernandez, Gloria P. Trujillo-Sánchez and Martha A. Hernandez-González

Algorithms 2026, 19(6), 440; https://doi.org/10.3390/a19060440 - 1 Jun 2026

Viewed by 637

Abstract

The correct classification of retinal lesions in retinal fundus images is important for supporting the analysis of diabetic retinopathy and age-related macular degeneration. State-of-the-art methods for this task are often based on black-box deep learning architectures that, despite their high performance, pose significant interpretability challenges, incur high computational costs, and lack computational interpretability at the feature-decision level. In this paper, a method based on surrogate-optimized features extracted by regular expressions is proposed for the classification of two retinal lesion categories (Drusen and Cotton Wool Spots). The method uses a compact and computationally interpretable row-by-row and column-by-column regular expression feature extractor together with a two-phase surrogate search over its discrete hyperparameters. Across 100 independent stratified executions under the repeated patch-level benchmark, the proposed method achieved a mean MCC of

0.7829 \pm 0.0448

, a mean accuracy of

0.9008 \pm 0.0217

, and a mean F1 score of

0.8529 \pm 0.0294

. The best execution reached an MCC of

0.8433

, an accuracy of

0.9286

, and a macro F1 score of

0.8966

, which was the highest result among the evaluated baselines within that same benchmark. Additional source–image disjoint grouped analyses were carried out as leakage-aware robustness checks under stricter source–image separation and to examine validation overfitting. Together, these analyses support the usefulness of the compact run-based descriptor under the present experimental conditions, while indicating that the two-phase search should be interpreted as a practical hyperparameter selection heuristic rather than as a statistically superior search strategy. Full article

(This article belongs to the Special Issue AI-Powered Biomedical Image Analysis)

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20 pages, 3154 KB

Open AccessArticle

A Data-Centric Algorithmic Pipeline for Enhancing Cardiac MRI Segmentation Using ViTUNeT and Quality-Aware Filtering

by Salvador de Haro, Jesús Cámara, Pilar González-Férez, José Manuel García and Gregorio Bernabé

Algorithms 2026, 19(3), 200; https://doi.org/10.3390/a19030200 - 6 Mar 2026

Viewed by 709

Abstract

The performance of deep-learning-based segmentation models is strongly dependent on the quality of the input data, which is frequently heterogeneous or degraded in real-world medical imaging scenarios. This work presents a data-centric algorithmic pipeline designed to improve cardiac MRI segmentation accuracy through systematic image enhancement and automatic slice-quality filtering. The proposed method is formalized as deterministic algorithm that combines image processing and supervised learning components. The approach integrates a contrast- and structure-preserving enhancement stage, based on bilateral filtering and adaptive histogram equalization, with a quality-aware selection algorithm. Slice quality is assessed using anatomical attributes extracted via YOLOv11s-based localization and a supervised classification model trained to identify diagnostically reliable images. When applied to transformer-based segmentation architectures such as ViTUNeT, the pipeline yields consistent improvements across all evaluation metrics without increasing model complexity or training cost. These findings emphasize the importance of algorithmic data curation as an effective strategy for enhancing robustness and stability in deep-learning segmentation pipelines and demonstrate the broader applicability of the proposed approach to computer-vision tasks involving heterogeneous or low-quality image datasets. Full article

(This article belongs to the Special Issue AI-Powered Biomedical Image Analysis)

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20 pages, 4501 KB

Open AccessArticle

Improving Prostate Cancer Segmentation on T2-Weighted MRI Using Prostate Detection and Cascaded Networks

by Nikolay Nefediev, Nikolay Staroverov and Roman Davydov

Algorithms 2026, 19(1), 85; https://doi.org/10.3390/a19010085 - 19 Jan 2026

Cited by 4 | Viewed by 1084

Abstract

Prostate cancer is one of the most lethal cancers in the male population, and accurate localization of intraprostatic lesions on MRI remains challenging. In this study, we investigated methods for improving prostate cancer segmentation on T2-weighted pelvic MRI using cascaded neural networks. We used an anonymized dataset of 400 multiparametric MRI scans from two centers, in which experienced radiologists had delineated the prostate and clinically significant cancer on the T2 series. Our baseline approach applies 2D and 3D segmentation networks (UNETR, UNET++, Swin-UNETR, SegResNetDS, and SegResNetVAE) directly to full MRI volumes. We then introduce additional stages that filter slices using DenseNet-201 classifiers (cancer/no-cancer and prostate/no-prostate) and localize the prostate via a YOLO-based detector to crop the 3D region of interest before segmentation. Using Swin-UNETR as the backbone, the prostate segmentation Dice score increased from 71.37% for direct 3D segmentation to 76.09% when using prostate detection and cropped 3D inputs. For cancer segmentation, the final cascaded pipeline—prostate detection, 3D prostate segmentation, and 3D cancer segmentation within the prostate—improved the Dice score from 55.03% for direct 3D segmentation to 67.11%, with an ROC AUC of 0.89 on the test set. These results suggest that cascaded detection- and segmentation-based preprocessing of the prostate region can substantially improve automatic prostate cancer segmentation on MRI while remaining compatible with standard segmentation architectures. Full article

(This article belongs to the Special Issue AI-Powered Biomedical Image Analysis)

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