High spatial resolution and large field of view mobile phone-based microscopy with adaptive phone screen illumination (Conference Presentation)
Bibliographic record
Abstract
Lens-based imaging approaches are faced with a trade-off between resolution and field-of-view (FOV). Generally, the greater the resolvable detail in a sample, the smaller the FOV we can observe. In our lab, we study the behaviour of microorganisms within confined spaces using microfluidic devices. In order to capture the full scope of their behaviour, we need to be able to discern individual microorganisms as well as observe the full microfluidic device area in real time. As such, visualizing such systems can be challenging, since we require an imaging system that can provide a resolution as high as 1 um, with a FOV large enough to fit our region of interest. To that end, we used the Nokia Lumia 1020 mobile phone, which has a 41.3 megapixel (MP) image sensor with a pixel size of 1.14 um, with an external lens attached to the camera for better focus, and we characterized the imaging system to have a spatial resolution of 1.2 um, with a FOV of 3.6 x 2.7 mm, and a working distance of 0.6 mm. Moreover, we used the screen of a Retina display Apple device as a versatile illumination source for this system. The screen is used to project various illumination patterns onto the specimen being imaged, each corresponding to a different illumination mode, with the Nokia phone capturing the resulting image. We tested our system by using it to image microorganisms such as Escherichia coli and Euglena gracilis within our microfluidic devices.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".