ArticleScientific reports2018
Deep transfer learning-based hologram classification for molecular diagnostics.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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
22 citing papers in PubMed, 65 citations in OpenAlex.
- Diffraction-informed deep learning for molecular-specific holograms of breast cancer cells.APL bioengineering · 2025Article
- Automated fast label-free quantification of cardiomyocyte dynamics with raw holograms for cardiotoxicity screening.Biomedical optics express · 2025Article
- Label-free imaging flow cytometry for cell classification based directly on multiple off-axis holographic projections.Journal of biomedical optics · 2025Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.Scientific reports · 2023Article
- On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning.Scientific reports · 2023Article
- Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.Artificial intelligence in medicine · 2023Article
- Multi-Class Classification and Multi-Output Regression of Three-Dimensional Objects Using Artificial Intelligence Applied to Digital Holographic Information.Sensors (Basel, Switzerland) · 2023Article
- Classification of Holograms with 3D-CNN.Sensors (Basel, Switzerland) · 2022Article
- 3D optical illusion as visualisation tools in spatial planning and development.Scientific reports · 2022Article
- Protocol for live cell image segmentation to profile cellular morphodynamics using MARS-Net.STAR protocols · 2022Article
- Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization.Light, science & applications · 2022Article
- Comprehensive deep learning model for 3D color holography.Scientific reports · 2022Article
- A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation.Scientific reports · 2021Article
- A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.Cell reports methods · 2021Article
- Classification of unlabeled cells using lensless digital holographic images and deep neural networks.Quantitative imaging in medicine and surgery · 2021Article
- Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Physical biology · 2021Review
- Towards image-based cancer cell lines authentication using deep neural networks.Scientific reports · 2020Article
- New technology on the horizon: Fast analytical screening technique FNA (FAST-FNA) enables rapid, multiplex biomarker analysis in head and neck cancers.Cancer cytopathology · 2020Review
- Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning.Annals of biomedical engineering · 2020Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images from the complex diffraction patterns produced by LDIH. This limits LDIH utility for point-of-care applications, particularly in resource limited settings. We describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses. Specifically, we captured holograms of cells labeled with molecular-specific microbeads and trained neural networks to classify these holograms without reconstruction. Using raw holograms as input, the trained networks were able to classify individual cells according to the number of cell-bound microbeads. The DTL-based approach including a VGG19 pretrained network showed robust performance with experimental data. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics.
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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.