ReviewMayo Clinic proceedings. Digital health2023
The Role of Geometry in Convolutional Neural Networks for Medical Imaging.
Review in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed.
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease.Mayo Clinic proceedings. Digital health · 2025Article
- Facial Recognition Algorithms: A Systematic Literature Review.Journal of imaging · 2025Review
- Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.Laboratory investigation; a journal of technical methods and pathology · 2024Review
- Topological Deep Learning: A New Dimension in Gastroenterology for Metabolic Dysfunction-Associated Fatty Liver.Cureus · 2024Article
- Predictors of Pathological Gastroesophageal Reflux among Emirati Patients with Reflux Symptoms Who Undergo Wireless pH Monitoring.Middle East journal of digestive diseases · 2023Article
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
Convolutional neural networks (CNNs) have played an important role in medical imaging-from diagnostics to research to data integration. This has allowed clinicians to plan operations, diagnose patients earlier, and study rare diseases in more detail. However, data quality, quantity, and imbalance all pose challenges for CNN training and accuracy; in addition, training costs can be high when many types of CNNs are needed in a health care system. Topology and geometry provide tools to ameliorate these challenges for CNNs when they are integrated into the CNN architecture, particularly in the data preprocessing steps or convolution layers. This paper reviews the current integration of geometric tools within CNN architectures to reduce the burden of large training datasets and offset computational costs. This paper also identifies fertile areas for future research into the integration of geometric tools with CNNs.
Identifiers
What OpenQuestion holds
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.