ArticlePloS one2021
Multi-view classification with convolutional neural networks.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers, 1 of them a synthesis that pooled it.
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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.
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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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in Plant Phenological Research: A Systematic Literature Review.Frontiers in plant science · 2022Pooled it
- Evaluating species identification apps as a tool for small plot-based surveys of vascular plants in Alberta, Canada.AoB PLANTS · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- A 3-dimensional Resnet model for assessment of drug efficacy in 3D cancer models using optical coherence tomography.PloS one · 2026Article
- MVBeetle: an interpretable multi-view deep learning model for fine-grained classification of Galerucinae and Alticinae (Coleoptera: Chrysomelidae).Frontiers in plant science · 2026Article
- Multi-view text classification through integrated RNN autoencoder learning of word, sentence, emotion and paragraph representations.Scientific reports · 2025Article
- UNet with self-adaptive Mamba-like attention and causal-resonance learning for medical image segmentation.Scientific reports · 2025Article
- Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring.Scientific reports · 2025Article
- A deep-learning model for predicting post-stroke cognitive impairment based on brain network damage.Quantitative imaging in medicine and surgery · 2025Article
- Automatic fused multimodal deep learning for plant identification.Frontiers in plant science · 2025Article
- Automated scoring of airway abnormalities and mucus plugging in chest magnetic resonance imaging of cystic fibrosis using artificial intelligence.Computational and structural biotechnology journal · 2025Article
- Enhancing Apple Cultivar Classification Using Multiview Images.Journal of imaging · 2024Article
- Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data.Briefings in bioinformatics · 2023Article
- MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.BMC medical informatics and decision making · 2023Article
- DeePSC: A Deep Learning Model for Automated Diagnosis of Primary Sclerosing Cholangitis at Two-dimensional MR Cholangiopancreatography.Radiology. Artificial intelligence · 2023Article
- Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions.Sensors (Basel, Switzerland) · 2023Article
- Dual-View Single-Shot Multibox Detector at Urban Intersections: Settings and Performance Evaluation.Sensors (Basel, Switzerland) · 2023Article
- "In the Wild" Video Content as a Special Case of User Generated Content and a System for Its Recognition.Sensors (Basel, Switzerland) · 2023Article
- FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses.Applied sciences (Basel, Switzerland) · 2023Article
- Multi-view image-based behavior classification of wet-dog shake in Kainate rat model.Frontiers in behavioral neuroscience · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans' decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object's class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extract and encode visual features from the multiple views and we propose strategies for fusing these information. More specifically, we investigate the following three strategies: (1) fusing convolutional feature maps at differing network depths; (2) fusion of bottleneck latent representations prior to classification; and (3) score fusion. We systematically evaluate these strategies on three datasets from different domains. Our findings emphasize the benefit of integrating information fusion into the network rather than performing it by post-processing of classification scores. Furthermore, we demonstrate through a case study that already trained networks can be easily extended by the best fusion strategy, outperforming other approaches by large margin.
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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.