ReviewBioMed research international2022
Dense Convolutional Network and Its Application in Medical Image Analysis.
Review in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis.Frontiers in big data · 2024Pooled it
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- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- A comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Leveraging large language models for gastrointestinal injury detection in athletes: a medical image analysis approach.Scientific reports · 2026Article
- RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images.Scientific reports · 2026Article
- Diagnoses of pulmonary embolism from non-contrast 4DCT using image processing-derived quantitative perfusion scores.npj biomedical innovations · 2026Article
- Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.Ophthalmology science · 2026Article
- MRI-based deep learning radiomics in predicting histological differentiation of oropharyngeal cancer: a multicenter cohort study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Clinically interpretable deep learning for pediatric dental age estimation with explainable AI.BMC oral health · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- Application of deep learning-clinical baseline feature fusion model to predict postoperative mortality in elderly patients with hip fracture: a multicenter study.Frontiers in medicine · 2026Article
- Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.Frontiers in aging neuroscience · 2026Article
- Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis.MethodsX · 2025Article
- AI-driven medical image analysis for sports injury diagnosis and prevention.Scientific reports · 2025Article
- Automated Detection and Grading of Renal Cell Carcinoma in Histopathological Images via Efficient Attention Transformer Network.Medical sciences (Basel, Switzerland) · 2025Article
- In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.Journal of imaging informatics in medicine · 2025Article
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.BMC medical imaging · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.
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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.