Automatic pathology of prostate cancer in whole mount slides incorporating individual gland classification
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an automatic pathology (AutoPath) approach to detect prostatic adenocarcinoma based on morphological analysis of high resolution whole mount (WM) histopathology images of the prostate. In the first stage of the cancer detection algorithm, a pre-screening of cancerous regions is performed at low magnification (5×) based on regional features. In the second stage, we propose a novel technique of labelling individual glands as benign or malignant using gland specific features at high magnification (20×). Two new features, Number of Nuclei Layers and Epithelial Layer Density, are proposed to label individual glands. We validate the approach on 70 WM slides, obtained from 30 patients, and achieve average sensitivity of 90%, specificity of 93% and accuracy of 93%. The main advantage of the approach is that detection of individual malignant gland units, irrespective of neighbouring histology and/or the spatial extent of the cancer, allows a finer annotation of cancer. The AutoPath method performs well on slides with low Gleason grades (3 and 4), but is currently limited in its ability to detect cancer in higher Gleason grades.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it