F-mode ultraviolet photoacoustic remote sensing for label-free virtual H&E histopathology using a single excitation wavelength
Bibliographic record
Abstract
Photoacoustic remote sensing (PARS) is a novel all-optical imaging modality that allows for non-contact detection of initial photoacoustic pressures. Using 266-nm excitation pulses, ultraviolet PARS (UV-PARS) has previously demonstrated imaging contrast for cell nuclei in histological samples with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo><</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>400</mml:mn> <mml:mspace width="thinmathspace"/> <mml:mspace width="thinmathspace"/> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> resolution. In prior PARS-based imaging schemes, the signal amplitude at an interrogation point was determined by the maximum deflection from the DC scattering signal in response to a pulsed excitation. This method, however, does not take into consideration additional information encoded in the frequency domain of the recorded PARS signals. Here, we present a frequency domain technique called F-mode PARS that can be used to generate images with nuclear and cytoplasmic enhanced contrast, enabling label-free virtual hematoxylin-and-eosin-like microscopy, using only a single excitation wavelength. With F-mode processing, we have been able to demonstrate contrast-to-noise ratios of up to 38 dB between cell nuclei and surrounding cytoplasm, which represents up to a 25-dB improvement over previous implementations of UV-PARS systems.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".