Label-free 3-D quantitative phase imaging cytometry with deep learning: identifying naive, memory, and senescent T cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it