CARE Foundation Models
Self-supervised AI trained on billions of single-cell images for morphological analysis
Company profile
Company profile
Deepcell is a life sciences technology company that combines artificial intelligence, high-resolution imaging, and microfluidics to decode cell identity and function through morphology. The company's flagship REM-I platform represents a breakthrough in single-cell analysis, enabling label-free imaging, AI-powered characterization, and gentle enrichment of viable cells based on their physical characteristics. At the heart of the platform is Deepcell's Human Foundation Model (HFM), a self-supervised AI trained on billions of single-cell images that extracts a 115-dimensional embedding from every brightfield cell image in real-time. This foundation model captures both human-interpretable morphological features and deeper patterns invisible to the eye, requiring no bespoke training for new applications or sample types. The platform combines high-speed, high-resolution brightfield imaging with gentle microfluidics for 6-way sorting, ensuring cells remain minimally perturbed and viable for downstream analysis. Deepcell pioneered the field of "morpholomics" — the high-dimensional, label-free study of cell morphology as a quantifiable data layer on par with genomics, transcriptomics, and proteomics. The company serves researchers across oncology, drug discovery, cell and gene therapy, regenerative medicine, transplant monitoring, and other areas of cell science. The integrated AXON data suite provides cloud and on-premises solutions for data visualization, analysis, and storage, enabling researchers to gain actionable insights within minutes without bioinformatics support. Founded from research at Stanford University, Deepcell continues to push the boundaries of cell science by revealing insights that no molecular marker or label could predict.
Company description DeepCell official website
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01 · Operating footprint
Company-profile context for clinical focus, customers, deployment, integration, milestones, and partnerships.
Embedded · Hybrid · On Premise deployment with recorded integration context.
Company milestones
Launch of REM-I OnSite (April 2026) for fully on-premises deployment in regulated and data-sensitive environments. Multiple pre-prints published on cancer cell diversity, sinonasal malignancy detection, and morphological applications. Attendance at AACR 2026 with keynote featuring Deepcell technology.
Partnerships
02 · Flagship portfolio
Selected source-backed Company products and the available commercial context.
Self-supervised AI trained on billions of single-cell images for morphological analysis
Integrated cloud & on-prem solution to explore, analyze and visualize morphology data
Fully on-premises single cell analysis for regulated and data-sensitive environments
AI-integrated platform for imaging, AI-powered analysis and enrichment of viable cells
03 · Research intelligence
Publications, preprints, clinical validation, and regulatory records in Evidence Position order.
Publications, clinical validation, and regulatory records connected to this Company appear here.
Research
NPJ precision oncology · Jun 22, 2026
Author Correction: COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.Communications biology · Oct 9, 2023
COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.Communications biology · Sep 22, 2023
Artificial Intelligence-Driven Morphology-Based Enrichment of Malignant Cells from Body Fluid.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · Aug 1, 2023
Erratum: Publisher's note: Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue.Journal of medical imaging (Bellingham, Wash.) · May 1, 2023
Deep learning-based method for segmenting epithelial layer of tubules in histopathological images of testicular tissue.Journal of medical imaging (Bellingham, Wash.) · May 1, 2023
Clinical validation
No linked records are currently available.
Regulatory
No linked records are currently available.
04 · Company-reported outcomes
Company-reported outcomes and customer stories are distinct from linked Research Intelligence evidence.
Label-free analysis without staining
Single-cell analysis of dissociated solid tumors
Cell line development acceleration with Gilead Sciences
Toxicity screening
Digital metrology for cell culture quality control to identify morphological drift in cell manufacturing
Detection of subtle AI-detected changes in cell shape and structure that human eyes and standard assays miss
Cell Line Development acceleration using AI-driven morphology prediction
Reduced clone identification time from 43 days to 36 days
Heart transplant rejection monitoring using peripheral blood morphotypes instead of invasive endomyocardial biopsies
Early rejection detection before tissue damage occurs using label-free imaging of peripheral blood
Drug-resistant phenotype identification in solid tumors using label-free imaging
Over 80% accuracy in identifying drug-resistant phenotypes
Rare cell identification with high reproducibility for batch processing consistency
Removed variability and enabled consistency in batch processing
Disease discovery and diagnosis validation
Validated and accelerated discovery and diagnosis of disease
05 · Leadership
Leadership profiles and professional links from the current Company record.
06 · Company updates
Recent articles published on DeepCell's official website.
07 · HAIC coverage
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08 · Official presence
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09 · Market pathway
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Market context
Companies applying AI to digital pathology, histopathology slide analysis, laboratory diagnostics, or biomarker discovery. Includes AI-powered interpretation of lab test results, at-home diagnostic devices with AI analysis, and platforms that analyze tissue samples or biological specimens to support clinical diagnosis.
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