Performance of a simple chromatin-rich segmentation algorithm in quantifying basal cell carcinoma from histology images
Bibliographic record
Abstract
BACKGROUND: The use of digital imaging and algorithm-assisted identification of regions of interest is revolutionizing the practice of anatomic pathology. Currently automated methods for extracting the tumour regions in basal cell carcinomas are lacking. In this manuscript a colour-deconvolution based tumour extraction algorithm is presented. FINDINGS: Haematoxylin and eosin stained basal cell carcinoma histology slides were digitized and analyzed using the open source image analysis program ImageJ. The pixels belonging to tumours were identified by the algorithm, and the performance of the algorithm was evaluated by comparing the pixels identified as malignant with a manually determined dataset.The algorithm achieved superior results with the nodular tumour subtype. Pre-processing using colour deconvolution resulted in a slight decrease in sensitivity, but a significant increase in specificity. The overall sensitivity and specificity of the algorithm was 91.0% and 86.4% respectively, resulting in a positive predictive value of 63.3% and a negative predictive value of 94.2% CONCLUSIONS: The proposed image analysis algorithm demonstrates the feasibility of automatically extracting tumour regions from digitized basal cell carcinoma histology slides. The proposed algorithm may be adaptable to other stain combinations and tumour types.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".