ArticleMedical & biological engineering & computing2020
Transfer learning for informative-frame selection in laryngoscopic videos through learned features.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Artificial intelligence-based image recognition in bronchoscopy: software development and randomized controlled trial for training evaluation in intensive care residents.BMC medical education · 2026Trial
- Development and validation of a deep learning model for identifying high-quality laryngoscopic images.Scientific reports · 2026Article
- Content oriented 3D-CNN sequence learning architecture for academic activities recognition using a realistic CAD dataset.Scientific reports · 2025Article
- Spatial-temporal attention for video-based assessment of intraoperative surgical skill.Scientific reports · 2024Article
- An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach.Journal of voice : official journal of the Voice Foundation · 2024Article
- Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.Medical & biological engineering & computing · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- [A multimodal medical image contrastive learning algorithm with domain adaptive denormalization].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection.Diagnostics (Basel, Switzerland) · 2023Article
- Transfer learning for medical image classification: a literature review.BMC medical imaging · 2022Review
- Automatic classification of informative laryngoscopic images using deep learning.Laryngoscope investigative otolaryngology · 2022Article
- Artificial intelligence in clinical endoscopy: Insights in the field of videomics.Frontiers in surgery · 2022Review
- Clinical value of video-assisted single-lumen endotracheal intubation and application of artificial intelligence in it.American journal of translational research · 2022Review
- Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks.Sensors (Basel, Switzerland) · 2021Article
- Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Narrow-band imaging (NBI) laryngoscopy is an optical-biopsy technique used for screening and diagnosing cancer of the laryngeal tract, reducing the biopsy risks but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to develop a deep-learning-based strategy for the automatic selection of informative laryngoscopic-video frames, reducing the amount of data to process for diagnosis. The strategy leans on the transfer learning process that is implemented to perform learned-features extraction using six different convolutional neural networks (CNNs) pre-trained on natural images. To test the proposed strategy, the learned features were extracted from the NBI-InfFrames dataset. Support vector machines (SVMs) and CNN-based approach were then used to classify frames as informative (I) and uninformative ones such as blurred (B), with saliva or specular reflections (S), and underexposed (U). The best-performing learned-feature set was achieved with VGG 16 resulting in a recall of I of 0.97 when classifying frames with SVMs and 0.98 with the CNN-based classification. This work presents a valuable novel approach towards the selection of informative frames in laryngoscopic videos and a demonstration of the potential of transfer learning in medical image analysis. Flowchart of the proposed approach to automatic informative-frame selection in laryngoscopic videos. The approach leans on the transfer learning process, which is implemented to perform learned-features extraction using different convolutional neural networks (CNNs) pre-trained on natural images. Frame classification is performed exploiting two different classifiers: support vector machines and fine-tuned CNNs.
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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.