Bibliographic record
Abstract
A single multimode fiber (MMF) provides almost an ideal optical channel to constitute a hair-thin endoscope for minimally invasive biomedical imaging at depths in tissue, especially if the imaging operation can be performed with one single shot in reflection mode, which, however, remains challenging to date. In this work, we present single-shot wide-field reflectance imaging by using a single MMF as the illumination unit and imaging probe simultaneously. To achieve single-shot image capture, a reflection matrix of the fiber was built by a learning-assisted approach for the universal inverse conversion from the output amplitudes to the input amplitudes. The performance was tested by imaging more than 30 000 natural scenes projected by a digital micromirror device, and an averaged Pearson correlation coefficient over 0.84 with respect to the ground truth was achieved in the experiment. Furthermore, the ability to image dynamic scenes at a high frame rate of up to 180 frames per second was demonstrated together with real-time observation of a freely moving microneedle located at the distal end of the MMF. The proposed reflection-mode single-fiber imaging scheme paves the way for practical video-rate microendoscopy at depths in tissue in a minimally invasive manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.533 |
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".