Microscopic Image Analysis and Recognition On Pathological Cells
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
According to the features of the configuration and color information on the cancer cells, an adaptive automatic threshold segmentation based on the RGB and HIS color spaces is presented, which is available to segment suspected cancer cells and nucleus from the complex backgrounds in the microscopic images. The edges of the suspected cancer cells and nucleus are detected by using Canny operator. Using the technology of eight-chain code tracking, the feature values of suspected cancer cells are extracted. The feature values consists of the perimeter, the area, the height, the width, the circularity, the rectangularity, the extension and the area ratio between the nucleus and the cytolympth. Based on feature values, a two-round recognition scheme combined with morphologic and colourometry is proposed to recognize and classify the pathological and normal cells. The results show that the proposed algorithm can efficiently segment cell images and receive higher accuracy of cancer cell diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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