Label-free 3-D quantitative phase imaging cytometry with deep learning: identifying naive, memory, and senescent T cells
Bibliographic record
Abstract
Abstract The current prevailing methods for identifying immune cell subsets exploit a group of differentiation markers (CDs) targeted by fluorochrome or metal conjugated antibodies. However, such labeling methods, requiring a staining process and specific reagents, prevent rapid and cost-effective identification of immune cell subsets. Therefore we developed a label-free imaging cytometry platform that synergistically used refractive index (RI) tomography and three-dimensional (3-D) deep learning. We constructed and trained a deep learning classifier that learns unique representations from the 3-D RI map of each cell obtained using RI tomography without labeling. In this study, we were able to classify human naïve, memory, and senescent T cells according to the expression of CD4, CD8, CD45RA, CCR7 and CD57 using the label-free classifier within milliseconds, with high precision (>95%) even though the morphological and biochemical characteristics extracted from the RI tomograms of the T cells are almost homogeneous. This cannot be achieved by conventional machine learning approaches that only exploit the set of manually extracted features. Our label-free cell sorting platform will facilitate rapid and cost-effective immunological and biomedical studies by eliminating the laborious labeling process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".