Detection and monitoring for cancer and abnormal vasculature by photoacoustic signal characterization of structural morphology
Bibliographic record
Abstract
Photoacoustic systems can produce high-resolution, high-contracts images of vascular structures. To reconstruct images at very high-resolution, signals must be collected from many transducer locations, which can be time consuming due to limitations in transducer array technology, In this thesis a method is presented to discriminate between normal and abnormal tissue based on the structural morphology of vasculature and permits data to be acquired quickly. To demonstrate that the approach may be useful for cancer detection, a special simulator that produces photoacoustic signal from 3D models of vascular tissue is developed. Validation of the simulator is performed against a derived exact equation for finite-length cylindrical photoacoustic sources and through FEM models. Results show that is possible to differentiate tissue classed even when it is not possible to resolve individual blood vessels. Performance of the algorithm remains strong as the number of transducer locations decreases and in the presence of noise.
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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".