ArticleNature machine intelligence2021
A Versatile Deep Learning Architecture for Classification and Label-Free Prediction of Hyperspectral Images.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hyperspectral imaging in oral oncology: a scoping review.Frontiers in oral health · 2026Pooled it
- Deep Learning Enables Identification of Antimicrobial Peptides Through Mechanochromic Fingerprints.Angewandte Chemie (International ed. in English) · 2026Article
- Deep unfolding blind source unmixing for multicolor fluorescence imaging.Biomedical optics express · 2026Article
- PLANCK: super-multiplex optical imaging without labeling.bioRxiv : the preprint server for biology · 2026Article
- Band Engineering and Structural-Geometrical Engineering in 2D/3D van der Waals Heterostructures for Advanced Photodetection and Intelligent Sensing.Nano-micro letters · 2026Review
- Methodologies for Assessing Chemical Toxicity to Aquatic Microorganisms: A Comparative Review.Molecules (Basel, Switzerland) · 2026Review
- DeepLuAd: Semantic-guided virtual histopathology of lung adenocarcinoma via stimulated Raman scattering.Theranostics · 2026Article
- Resolution-Adaptive Binning Enhances Machine Learning Modeling by Interbatch and Multiplatform Orbitrap-Based Shotgun Mass Spectrometry Data Integration.Analytical chemistry · 2025Article
- Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging.Newton ((New York, N.Y.) · 2025Article
- Article
- Label-free metabolic fingerprinting of motile mammalian spermatozoa with subcellular resolution.BMC biology · 2025Article
- Fast segmentation and multiplexing imaging of organelles in live cells.Nature communications · 2025Article
- Research on a Burn Severity Detection Method Based on Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2025Article
- Machine learning empowered coherent Raman imaging and analysis for biomedical applications.Communications engineering · 2025Review
- Noninvasive Oral Hyperspectral Imaging-Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study.Journal of medical Internet research · 2025Article
- Progress in the application of hyperspectral imaging technology in quality detection and in the modernization of Chinese herbal medicines.Frontiers in chemistry · 2025Review
- Hyperspectral Information Extraction With Full Resolution From Arbitrary Photographs.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Predicting maternal risk level using machine learning models.BMC pregnancy and childbirth · 2024Article
- Advances in Super-resolution Stimulated Raman Scattering Microscopy.Chemical & biomedical imaging · 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
4 authors.
Funding
Abstract
Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.
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.