ArticleProceedings of SPIE--the International Society for Optical Engineering2015
Toward Content Based Image Retrieval with Deep Convolutional Neural Networks.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.Acta odontologica Scandinavica · 2025Article
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Automatic deep learning detection of overhanging restorations in bitewing radiographs.Dento maxillo facial radiology · 2024Article
- Classification of mandibular molar furcation involvement in periapical radiographs by deep learning.Imaging science in dentistry · 2024Article
- A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition.Children (Basel, Switzerland) · 2024Article
- Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning.Frontiers in oncology · 2024Article
- Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons.Open life sciences · 2024Review
- Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph.Journal of dental sciences · 2024Article
- Review
- Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Caries detection with tooth surface segmentation on intraoral photographic images using deep learning.BMC oral health · 2022Article
- Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs.Dento maxillo facial radiology · 2022Article
- A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2022Article
- A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs.BioMed research international · 2022Article
- Progress in diagnosis of bone metastasis of prostate cancer.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2021Article
- A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.The British journal of radiology · 2021Article
- Searching for pneumothorax in x-ray images using autoencoded deep features.Scientific reports · 2021Article
- Human embryonic stem cell classification: random network with autoencoded feature extractor.Journal of biomedical optics · 2021Article
- Similar image search for histopathology: SMILY.NPJ digital medicine · 2019Article
- Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.Journal of periodontal & implant science · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128×128 to an output encoded layer of 4×384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This prelimainry effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.
Indexed as
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.