ReviewInsights into imaging2023
Topological data analysis in medical imaging: current state of the art.
Review in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed.
- Article
- Multimodal Ultrasound-Based Decision Support for Bethesda IV Thyroid Nodules: Integration of Clinical, Radiomics, Deep Learning, and Topological Features.Journal of imaging informatics in medicine · 2026Article
- Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics, and Generative AI.Bioengineering (Basel, Switzerland) · 2026Review
- Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.BMC medical imaging · 2026Article
- Manifold topological deep learning for biomedical data.Nature communications · 2026Article
- Topo-CNN: Retinal Image Analysis with Topological Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Disrupted Higher-Order Topology in OCD Brain Networks Revealed by Hodge Laplacian - an ENIGMA Study.bioRxiv : the preprint server for biology · 2026Article
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
- Topological feature-driven TabPFN model for prediction of enlarged hemorrhage and edema after tumor resection in meningiomas.Frontiers in medicine · 2026Article
- GRAPHOS re-examined: graph-theoretic features do not improve dyslexia screening, and a demographic confound in the Dytective dataset.Frontiers in digital health · 2026Article
- CT habitat radiomics and topological data analysis based on interpretable machine learning for prediction of pancreatic ductal adenocarcinoma pathological grading.BMC medical imaging · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis.Research square · 2025Article
- Machine learning of time series data using persistent homology.Scientific reports · 2025Article
- Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease.Mayo Clinic proceedings. Digital health · 2025Article
- Topological Data Analysis in the Assessment of Coronary Atherosclerosis: A Comprehensive Narrative Review.Mayo Clinic proceedings. Digital health · 2025Review
- A Robust Multivariate Time Series Classification Approach Based on Topological Data Analysis for Channel Fault Tolerance.Sensors (Basel, Switzerland) · 2025Article
- Manifold Topological Deep Learning for Biomedical Data.Research square · 2025Article
- Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.European journal of nuclear medicine and molecular imaging · 2025Article
- Persistence Image from 3D Medical Image: Superpixel and Optimized Gaussian Coefficient.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.