ArticleNature protocols2021
Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.
Article in Nature protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Evaluating Isochoric Freezing as a Strategy for Storage of Red Blood Cells.Biotechnology and bioengineering · 2026Article
- LFC-Lab on a chip · 2026Article
- Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.Scientific reports · 2025Article
- Review
- Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.Annals of hematology · 2025Article
- Imaging flow cytometry: from high - resolution morphological imaging to innovation in high - throughput multidimensional biomedical analysis.Frontiers in bioengineering and biotechnology · 2025Review
- Using deep learning to decipher the impact of telomerase promoter mutations on the dynamic metastatic morpholome.PLoS computational biology · 2024Article
- Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.International journal of molecular sciences · 2024Article
- Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.Scientific reports · 2024Article
- Application of unimodal probability distribution models for morphological phenotyping of budding yeast.FEMS yeast research · 2024Article
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies.Nature communications · 2023Article
- Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.The New phytologist · 2023Article
- Machine learning implementation strategy in imaging and impedance flow cytometry.Biomicrofluidics · 2023Review
- Continuous microfluidic flow-through protocol for selective and image-activated electroporation of single cells.RSC advances · 2023Article
- Touch-free optical technologies to streamline the production of T cell therapies.Current opinion in biomedical engineering · 2023Article
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Imaging flow cytometry: a primer.Nature reviews. Methods primers · 2022Article
- Labels in a haystack: Approaches beyond supervised learning in biomedical applications.Patterns (New York, N.Y.) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.
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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.