Bibliographic record
Abstract
Deep learning algorithms have made phenomenal inroads into the domain of medical image formation and analysis, including in segmentation of cervical cell cytology images. Cervical cancer is one of the most common cancers affecting women worldwide. Medical imaging plays a pivotal role in the detection and diagnosis of cervical cancer. Segmentation of overlapping cells is one of the major challenges in analyzing these images owing to varying contrast of cell cytoplasm and wide range of cell overlap ratios. The next step in this domain is to segment the overlapped portion of cervical cells in order to harness the full scope of cellular information. We propose to adopt the Meta-Polyp segmentation method for our task which combines the MetaFormer baseline model elements such as the neural network transformers with U-Net architecture with some minor tweaks in hyperparameters. The Meta-Polyp neural network is able to capture the overlapped region of cervical cells to an extent. We then refine our segmentation results by applying a modified U-Net neural network which employs transfer learning in its encoder stage to get the final segmentation masks. Using this novel deep learning image segmentation pipeline we get accurate results for the segmentation of overlapped region of cervical cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".