ArticleScientific reports2021
Converting tabular data into images for deep learning with convolutional neural networks.
Article in Scientific reports, 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 48 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.
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
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.BMC medical informatics and decision making · 2022Pooled it
- Application Progress of Machine Learning in Prognostic Prediction of Percutaneous Coronary Intervention: A Systematic Review.Journal of clinical medicine research · 2026Review
- Mol2Image: an enhanced DDI prediction framework leveraging drug molecular descriptors.BMC bioinformatics · 2026Article
- [Medical prior-guided TabMap deep learning model for ovarian cancer prediction and interpretability analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- FedVI: Financial Cross-Domain Federated Learning with Scarce Overlapping Samples via Visual Representation of Heterogeneous Tabular Data and Meta-Optimization.Entropy (Basel, Switzerland) · 2026Article
- The adaptive engagement framework: enhancing banking customer experience through AI-powered invisible marketing.Scientific reports · 2026Article
- Predicting COVID-19 Mortality Risk Among Cardiovascular Disease Patients Using Artificial Intelligence Algorithms: A Retrospective Study on Clinical Data.Health science reports · 2026Article
- Interpretable Feature Selection and Hybrid Deep Learning Models for Depressive Symptoms Prediction from Wearable Device Data.Journal of medical systems · 2026Article
- Neural Latent Filtering for Gene Discovery in Breast Cancer Subtypes.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- Development of an Intelligent Clinical Decision Support System for Spirometry Quality Control.Diagnostics (Basel, Switzerland) · 2026Article
- Image generator for tabular data based on non-Euclidean metrics for CNN-based classification.PloS one · 2026Article
- Optimized federated learning framework with RegNetZ and Swin-Transformer for multimodal pancreatic cancer detection1.Scientific reports · 2025Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study.JMIR medical informatics · 2025Article
- Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.Scientific reports · 2025Article
- Deep Learning of Cellular Metabolic Flux Distributions Predicts Lifespan.bioRxiv : the preprint server for biology · 2025Article
- Transforming tabular data into images via enhanced spatial relationships for CNN processing.Scientific reports · 2025Article
- Interpretable discovery of patterns in tabular data via spatially semantic topographic maps.Nature biomedical engineering · 2025Article
- Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.Briefings in bioinformatics · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.
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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.