Hyperspectral imaging and spectral unmixing of stained tissue sections using a spectrally programmable light engine
Bibliographic record
Abstract
A series of hyperspectral transmission images of hematoxylin and eosin stained tissue sections from cervical biopsies were acquired at 10 nm intervals and assembled into a hyperspectral image cube. Custom software providing extraction of spectra at each pixel allows selection of images with maximum contrast for determination of selected features and differentiation of tissue features. Illumination profiles were created using a spectrally and temporally programmable light engine based on a spatial light modulator that can dynamically create any narrow or broadband spectral profile was used to select illumination wavelengths. Images were acquired with a monochrome CCD camera. Several methods of combining images from individual or composite spectral bands to recreate color images for pathologist review are shown. Unlike current "mechanical" illumination systems employing optical filters, filter wheels, motors, shutters and multiple control interfaces, the light engine integrates the lamp, wavelength control, intensity control and exposure control in a simple MEMS based system, where the only moving part is the lamp cooling fan. Illumination can now be programmed dynamically with digital control of all illumination parameters allowing wavelengths and intensities to be changed much faster than with filter wheels, and providing exposure control orders of magnitude more precise than mechanical shutters. This system can be integrated with digital imaging systems. Digitally controlled illumination is bit additive with image data providing high dynamic range imaging with monochrome or with color imaging devices. Performance of image analysis software for nuclear morphometric and tissue architecture analysis are compared for different wavelength regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".