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Mathematics
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Recent Advances in Machine Learning Methods for Medical Imaging Analysis
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Recent Advances in Machine Learning Methods for Medical Imaging Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2689

Special Issue Editors

Dr. Xiaopeng Yang

E-Mail Website
Guest Editor
School of Global Entrepreneurship and Information Communication Technology, Handong Global University, Pohang 37554, Gyeongbuk, Republic of Korea
Interests: computer vision; medical image processing; machine learning; deep learning; human–computer interaction
Dr. Danila Germanese

E-Mail Website
Guest Editor
Institute of Information Science and Technologies (ISTI), National Research Council (CNR), 56124 Pisa, Italy
Interests: biomedical signals; images processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning, especially deep learning, has become prevalent in medical imaging analysis to assist in the detection, diagnosis, and treatment of various diseases. Despite the success of deep learning in medical imaging analysis, the main bottleneck in medical imaging analysis is the lack of large-sized and properly annotated datasets, which are very costly to establish. Recently, many studies have attempted to use various mathematical models to improve deep learning to address this challenge. In particular, semi-supervised, self-supervised, and unsupervised deep learning algorithms have showed promising results in medical imaging analyses, including image registration, detection, classification, and segmentation. Papers focused on using novel machine learning algorithms to solve problems in medical imaging analysis are welcome to be submitted to this Special Issue.

Dr. Xiaopeng Yang
Dr. Danila Germanese
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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

  • machine learning
  • medical imaging analysis
  • computer vision
  • deep learning
  • supervised learning
  • unsupervised learning
  • self-supervised learning
  • semi-supervised learning
  • classification
  • segmentation
  • registration
  • detection
  • attention
  • vision transformer

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Published Papers (3 papers)

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Research

28 pages, 2584 KB  
Article
Improving Cross-Domain Generalization in Brain MRIs via Feature Space Stability Regularization
by Shawon Chakrabarty Kakon, Harishik Dev Singh Jamwal and Saurabh Singh
Mathematics 2026, 14(6), 1082; https://doi.org/10.3390/math14061082 - 23 Mar 2026
Viewed by 605
Abstract
Deep learning models for brain tumor classification from magnetic resonance imaging (MRI) often achieve high in-dataset accuracy but exhibit substantial performance degradation when evaluated on unseen clinical data due to domain shift arising from variations in imaging protocols and intensity distributions. Existing approaches [...] Read more.
Deep learning models for brain tumor classification from magnetic resonance imaging (MRI) often achieve high in-dataset accuracy but exhibit substantial performance degradation when evaluated on unseen clinical data due to domain shift arising from variations in imaging protocols and intensity distributions. Existing approaches largely rely on architectural scaling or parameter-level regularization, which do not explicitly constrain the stability of learned feature representations. This manuscript proposes Feature Space Stability Regularization (FSSR), a lightweight and model-agnostic training framework that enforces consistency in latent feature representations under realistic, MRI-safe-intensity perturbations. FSSR introduces an auxiliary feature space loss that minimizes the ℓ2 distance between normalized embeddings extracted from the input MRI images and their intensity-perturbed counterparts, alongside standard cross-entropy supervision. This manuscript evaluated FSSR across three convolutional backbones, ResNet-18, ResNet-34, and DenseNet-121, trained exclusively on the Kaggle Brain MRI dataset. Feature space analysis demonstrates that FSSR consistently reduces mean feature deviation and variance across architectures, indicating more stable internal representations. Generalization is assessed via zero-shot evaluation on the fully unseen BRISC-2025 dataset without retraining or fine-tuning. On the source domain, the best-performing configuration achieves 97.71% accuracy and 97.55% macro-F1. Under domain shift, FSSR improves external accuracy by up to 8.20 percentage points and the macro-F1 by up to 12.50 percentage points, with DenseNet-121 achieving a 96.70% accuracy and 96.87% macro-F1 at a domain gap of only 0.94%. Confusion matrix analysis further reveals the reduced class confusion and more stable recall across challenging tumor categories, demonstrating that feature-level stability is a key factor for robust brain MRI classification under domain shift. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Medical Imaging Analysis)
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29 pages, 1953 KB  
Article
JDC-DA: An Unsupervised Target Domain Algorithm for Alzheimer’s Disease Diagnosis with Structural MRI Using Joint Domain and Category Dual Adaptation
by Yuan Sui, Yujie Zhang, Ying Wei and Gang Yang
Mathematics 2026, 14(6), 1067; https://doi.org/10.3390/math14061067 - 21 Mar 2026
Viewed by 244
Abstract
Domain shift in multi-source MRI imaging data significantly degrades the performance of Alzheimer’s disease diagnostic models. This study aims to develop an effective unsupervised domain adaptation method to enhance diagnostic accuracy across different clinical datasets. We propose a Joint Domain and Category Dual [...] Read more.
Domain shift in multi-source MRI imaging data significantly degrades the performance of Alzheimer’s disease diagnostic models. This study aims to develop an effective unsupervised domain adaptation method to enhance diagnostic accuracy across different clinical datasets. We propose a Joint Domain and Category Dual Adaptation framework (JDC-DA) that integrates metric learning and adversarial learning. The method employs multi-scale feature aggregation to capture diverse lesion characteristics, generates dynamic prototype features through category clustering, and implements a novel metric learning approach that simultaneously aligns both domain-level and category-level feature distributions. Additionally, we introduce a classification certainty maximization strategy that establishes a dual adversarial mechanism between domain discriminator and classification discrepancy discriminator. The framework was evaluated on four public datasets (ADNI-1, ADNI-2, ADNI-3, AIBL) containing 1230 baseline sMRI scans for four classification tasks: AD vs. NC, MCI vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. The proposed JDC-DA method achieved superior performance with accuracies of 92.16%, 83.56%, 81.96%, and 79.12% for the four classification tasks respectively, significantly outperforming existing state-of-the-art domain adaptation methods across all evaluation metrics. The JDC-DA framework effectively addresses domain shift challenges in Alzheimer’s disease diagnosis through its integrated approach to feature alignment and adversarial learning. The method demonstrates strong potential for clinical application in automated diagnosis systems, particularly for handling multi-center neuroimaging data with distribution discrepancies. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Medical Imaging Analysis)
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12 pages, 1376 KB  
Article
Deep Learning Model with Attention Mechanism for a 3D Pancreas Segmentation in CT Scans
by Idriss Cabrel Tsewalo Tondji, Camilla Scapicchio, Francesca Lizzi, Maria Evelina Fantacci, Piernicola Oliva and Alessandra Retico
Mathematics 2025, 13(24), 3942; https://doi.org/10.3390/math13243942 - 11 Dec 2025
Viewed by 1213
Abstract
Accurate segmentation of the pancreas in Computed Tomography (CT) scans is a challenging task, which may be crucial for the diagnosis and treatment planning of pancreatic cancer. The irregular shape of the pancreas, its low contrast relative to surrounding tissues, and its close [...] Read more.
Accurate segmentation of the pancreas in Computed Tomography (CT) scans is a challenging task, which may be crucial for the diagnosis and treatment planning of pancreatic cancer. The irregular shape of the pancreas, its low contrast relative to surrounding tissues, and its close proximity to other complex anatomical structures make it difficult to accurately delineate its contours. Furthermore, a significant class imbalance between foreground (pancreas) and background voxels in an abdominal CT series represents an additional challenge for deep-learning-based approaches. In this study, we developed a deep learning model for automated pancreas segmentation based on a 3D U-Net architecture enhanced with an attention mechanism to improve the model capability to focus on relevant anatomical features of the pancreas. The model was trained and evaluated on two widely used benchmark datasets for volumetric segmentation, the NIH Healthy Pancreas-dataset and the Medical Segmentation Decathlon (MSD) pancreas dataset. The proposed attention-guided 3D U-Net achieved a Dice score of 80.8 ± 2.1%, ASSD of 2.1 ± 0.3 mm, and HD95 of 8.1 ± 1.6 mm on the NIH dataset, and the values of 78.1 ± 1.1%, 3.3 ± 0.3 mm, and 12.3 ± 1.5 mm for the same metrics on the MSD dataset, demonstrating the value of attention mechanisms in improving segmentation performance in complex and low-contrast anatomical regions. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning Methods for Medical Imaging Analysis)
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