Optimization of Lensless Imaging Using Ray Tracing
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
Lensless microscopy is a well-established imaging approach that replaces traditional lenses with phase modulators, enabling compact, low-cost, and computationally driven analysis of biological samples. In this work, we show how ray tracing simulations can be used to optimize lensless imaging systems for automated classification, particularly for detecting red blood cell (RBC) disease. Rather than improving the machine learning classification algorithm, our focus is on refining optical parameters such as element spacing and modulator type to maximize classification performance. We modeled a lensless microscope in Zemax OpticStudio (ray tracing) and compared the results against Fourier optics simulations. Despite not explicitly modeling diffraction, ray tracing produced classification results largely consistent with wave optics simulations, confirming its effectiveness for parameter optimization in lensless imaging setups used for classification tasks. Furthermore, to show the flexibility of the ray tracing model, we introduced a microlens array (MLA) as the phase modulator and performed the classification task on the generated patterns. These results establish ray tracing as an efficient tool for the optical design of lensless microscopy systems intended for machine learning based biomedical applications. The developed lensless microscopy model enables the generation of datasets for training neural networks.
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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