ReviewFrontiers in oncology2021
Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis.
Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 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.
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Who cites it
84 citing papers in PubMed.
- Deep learning-based automatic identification model of main pancreatic duct in intraoperative ultrasonography.Surgical endoscopy · 2026Article
- Semantic Segmentation by Semantic Proportions.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Assisted Accuracy Improvement in Bladder Cancer Staging of Spectrum-Aided Visual Enhanced Cystoscopy Images.Biosensors · 2026Article
- Automatic Detection of Vegetations With Transesophageal Echocardiography in Infective Endocarditis Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Observational
- Standardizing Vegetation Size Measurement in Native Left-Sided Infective Endocarditis Using Artificial Intelligence.Journal of clinical medicine · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Deep learning for automatic segmentation of the inferior alveolar nerve using a hybrid CNN-transformer framework.Scientific reports · 2026Article
- Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using Deep Learning.The Laryngoscope · 2026Article
- Transferring thrombus labels from CTPA to non-contrast CT via accurate pulmonary vessel registration: a critical step toward AI-based pulmonary embolism detection.Quantitative imaging in medicine and surgery · 2026Article
- Vehicle component defect detection method based on transformer and multi-scale feature fusion.Scientific reports · 2026Article
- Deep learning-based non-invasive prediction of axillary lymph node metastasis in breast cancer: performance of the YOLO-v11 object detection algorithm.BMC medical imaging · 2026Article
- Evaluation of deep learning models for segmentation of hippocampus volumes from MRI images in Alzheimer's disease.Scientific reports · 2026Article
- Development and evaluation of clinical pregnancy prediction models for intrauterine insemination using three machine learning algorithms.Frontiers in endocrinology · 2026Article
- Applicability and performance of convolutional neural networks for the identification of periodontal bone loss in periapical radiographs: a scoping review.Oral radiology · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Current applications of deep learning in vertebral fracture diagnosis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- A Computer Vision and Machine Learning Approach to Classify Views in Distal Radius Radiographs.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Review
- Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer's Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning.International journal of molecular sciences · 2025Article
- Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).Journal of imaging informatics in medicine · 2025Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.
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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.