Automated segmentation of the epidermis area in skin whole slide histopathological images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the development of high‐speed, high‐resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer‐aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer‐aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use.
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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 | 0.001 | 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 | 0.000 | 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