ReviewComputational and mathematical methods in medicine2023
A Review Paper about Deep Learning for Medical Image Analysis.
Review in Computational and mathematical methods in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.
- Convolutional neural networks for medical imaging in resource-constrained settings: a scoping review of architectures, performance, and deployment challenges.Frontiers in artificial intelligence · 2026Pooled it
- LabCora: label correlation-aware semi-supervised multi-label medical image classification.Medical & biological engineering & computing · 2026Article
- FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.European journal of nuclear medicine and molecular imaging · 2026Article
- Comparison of dual-energy X-ray absorptiometry and lumbar radiographs using deep learning for osteoporosis diagnosis.Journal of bone and mineral metabolism · 2026Article
- Deep Learning-Assisted Accuracy Improvement in Bladder Cancer Staging of Spectrum-Aided Visual Enhanced Cystoscopy Images.Biosensors · 2026Article
- A deep learning PET/CT biomarker for early progression (POD24) and survival stratification in follicular lymphoma: a multicenter study.European journal of nuclear medicine and molecular imaging · 2026Article
- Leveraging large language models for gastrointestinal injury detection in athletes: a medical image analysis approach.Scientific reports · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- When Lie Groups Meet Hyperspectral Images: Equivariant Manifold Network for Few-Shot HSI Classification.Sensors (Basel, Switzerland) · 2026Article
- Hybrid EfficientNet B4 and SVM framework for rapid and accurate bone cancer diagnosis from X-rays.Scientific reports · 2026Article
- AI-driven diagnosis of acute aortic syndrome based on multi-modal information fusion.Scientific reports · 2026Article
- Automated segmentation of COVID-19 lesions in CT scans using attention U-net with hybrid loss functions.Scientific reports · 2026Article
- State of the Art of Remote Sensing Data: Gradient Pattern in Pseudocolor Composite Images.Journal of imaging · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Deep learning and generative AI for medical imaging and clinical decision support systems: a structured critical review.Frontiers in digital health · 2026Review
- Hybrid GAN-LSTM framework for diabetic foot ulcer image synthesis and automated diagnosis.Frontiers in medicine · 2026Article
- Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT.Frontiers in bioengineering and biotechnology · 2026Article
- Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.Frontiers in medicine · 2026Review
- RadioGuide-DCN: A Radiomics-Guided Decorrelated Network for Medical Image Classification.Bioengineering (Basel, Switzerland) · 2025Article
- AI-driven medical image analysis for sports injury diagnosis and prevention.Scientific reports · 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
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction.
Indexed as
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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.