Beyond phase signals: digital holographic microscopy and AI revealing disease-specific cell phenotypes
Bibliographic record
Abstract
Digital holographic microscopy (DHM) has emerged as a powerful quantitative phase imaging technique offering label-free, non-invasive visualization of cell structures and dynamics. Specifically, DHM provides a quantitative phase signal (QPS) that is highly sensitive, particularly to dry mass, which has led to the development of attractive applications in cell biology. QPS contains, in an intricate way, a large amount of information about the cell content and morphology. Its interferometric detection gives it a high degree of sensitivity, but at the same time it is contaminated with a coherent noise that makes a precise analysis of cell information it contains difficult. I’ll present a series of technical developments that have enabled us to obtain a quasi-coherent noise-free QPS from which we can extract a set of cellular parameters. This paves the way for high-content, label-free screening to identify disease-specific cell phenotypes. Some applications related to neuropsychiatric diseases will be presented. Finally, it will be shown how AI can take advantage of these technical developments to enable label-free cell phenotyping without the need for cumbersome instrumentation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".