Advances in lensless imaging based on machine learning using ray tracing
Bibliographic record
Abstract
Sickle cell disease (SCD), an inherited red blood cell disorder characterized by the cell’s sickle shape, affects nearly 100 million people worldwide. Microscopy of red blood cells is crucial for disease detection. However, conventional methods often rely on bulky, expensive devices and require skilled human analysts to interpret Red Blood Cell (RBC) images. To address these challenges, we replaced the lens of the microscope with a phase modulator to capture the opto-biological signature(OBS) of blood samples. Our approach bypasses the need for image reconstruction, enabling direct classification using Machine Learning (ML) algorithms based on statistical features extracted from OBS data. In this project, we focus on optimizing the experimental setup using ray tracing software simulations to enhance the efficiency and accuracy of our classification system, rather than optimizing the ML algorithm itself. In this paper, we have shown how optical design can effectively help enhance the performance of lensless imaging systems. We utilized Zemax OpticStudio for ray tracing simulations to model the lensless microscope and subsequently replicated these simulations using Fourier optics in MATLAB. The advantages of using ray tracing software like Zemax include its specialized environment for optical simulations and the efficiency in handling optical components. Additionally, Zemax provides a well-established framework for ensuring accuracy, whereas MATLAB simulations may vary based on the quality of the code and algorithms used. In conclusion, depending on the specific application of the lensless microscope, parameters such as the distances between elements and the type of modulator can be effectively optimized using a ray tracing software like Zemax.
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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".