Automated prostate glandular and nuclei detection using hyperspectral imaging
Bibliographic record
Abstract
Detection and segmentation of glandular structures are important, since these structures contain clinical relevant information regarding the disease status and Gleason grade of prostate cancer. Manual gland segmentation process is very time consuming and subjective, also existing automated methods are not robust and reliable. We set out to design an automated, fast and objective method. In this paper we present an automated methodology for automated detection of structures of interest in digitalized histopathology images of a Tissue Micro Array (TMA). We show a successful method for detection of prostate glandular structures and its nuclei. Our method integrates different techniques: (1) construct hyperspectral transmission images using sixteen light wavelengths, (2) use Principal Component Analysis (PCA) to construct new RGB images, (3) use clustering to segment different structures in an unsupervised fashion, and (4) apply post-processing morphological cleaning as the final step in our pipeline. We detected 80% plus of the glandular structure in 61% of cores, 80% -50% of the glands in 15% of cores and less than 50% of the glands in 24% of cores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".