ArticleScientific reports2024
Novel applications of Convolutional Neural Networks in the age of Transformers.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.Cureus · 2026Review
- Feature Extraction and Unsupervised Classification of Roadway Fracture Signals: A Full-Section Wi-Fi Wireless Monitoring Approach.Sensors (Basel, Switzerland) · 2026Article
- Article
- AI-Guided Discovery of Oncogenic Signaling Crosstalk in Tumor Progression and Drug Resistance.Oncology research · 2026Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Decoding information stored in DNAs through single-molecule electrical detection platforms.Innovation (Cambridge (Mass.)) · 2025Review
- Review
- Maize yield estimation in Northeast China's black soil region using a deep learning model with attention mechanism and remote sensing.Scientific reports · 2025Article
- Enhancing Stability and Performance of Conductive Bridge Random Access Memory: Use of a Copper-Doped ZnO Nanorod-Embedded Switching Layer.ACS applied materials & interfaces · 2025Article
- An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions.Sensors (Basel, Switzerland) · 2025Review
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.Current neuropharmacology · 2025Review
- Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches.International journal of molecular sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Convolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial Intelligence. However, in recent years newer architectures such as Transformers have dominated both research and practical applications. While CNNs still play critical roles in many of the newer developments such as Generative AI, they are far from being thoroughly understood and utilised to their full potential. Here we show that CNNs can recognise patterns in images with scattered pixels and can be used to analyse complex datasets by transforming them into pseudo images with minimal processing for any high dimensional dataset, representing a more general approach to the application of CNNs to datasets such as in molecular biology, text, and speech. We introduce a pipeline called DeepMapper, which allows analysis of very high dimensional datasets without intermediate filtering and dimension reduction, thus preserving the full texture of the data, enabling detection of small variations normally deemed 'noise'. We demonstrate that DeepMapper can identify very small perturbations in large datasets with mostly random variables, and that it is superior in speed and on par in accuracy to prior work in processing large datasets with large numbers of features.
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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.