Abstract A28: Improving detection through SELF (selective excitation light fluorescence) imaging
Bibliographic record
Abstract
Abstract The use of fluorescence imaging and spectroscopy for the detection of early neoplastic epithelial lesions has been well established, with clinically adopted devices in for use in the lung (Onco-LIFE, SAFE-1000, etc), the oral cavity (VELScope and Identaf 3000) and others in development for the cervix and dermatology. Primarily these imaging technologies make use of fluorescence excitation illumination of a single wavelength (predominately in the near UV to blue wavelength ranges) and differentiate between normal and not normal tissue based upon intensity changes and spectral shifts in the emitted tissue autofluorescence (not normal -darker with less green or blue relative the red fluorescence). SELF imaging makes use of a multitude of illumination wavelengths to specifically couple to the action spectra of the fluorophores within the tissue to enhance the differentiation between tissue states, fluorophores and their immediate environment. This methodology makes use of the different absorption spectra (action spectra) of different fluorophores or similar flourophores in different environments. Conventionally this can be done through the sequential illumination with many different excitation wavelengths and sequential image capture, to collect a hyperspectral excitation image data cube followed by some form of spectra unmixing to resolve the individual targets (components) contributing to the image. Each target is identified by a unique weighted sum of pixel intensities across the excitation wavelengths in the data cube. Through the use of a programmable light source such as the OneLight (OneLight Corp.) in which not only the wavelengths of the illumination light, but their individual intensities (alone or in combination) can be selected under computer control it is possible to not only rapidly illuminate with separate excitation wavelengths but to illuminate with a collection of weighted (each wavelength has a different selected intensity) spectra to specifically couple to selected fluorescence targets (specific fluorophores or fluorophores in specific local environments). Thus instead of needing to illuminate with a series of 10 separate excitation wavelengths and collect separate 10 images on can illuminate with a few (2–3) weighted profiles of excitation wavelengths and collect a few (2–3) images, the number of spectra used (images collected) determines the number of targets differentiated. In this fashion it is possible to detect in an image many more specific flourophore types than with conventional fluorescence imaging. SELF imaging in microscopy, wide field macroscopic imaging and ex vivo and in vivo imaging has been demonstrated and will be presented. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A28.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".