A Cervical Cell Classification Framework Based on Multiview Supervised Contrastive Learning
Bibliographic record
Abstract
Objectives: Cervical cell classification is fundamental for early cervical cancer detection. Deep convolutional neural networks (CNNs) have made great progress in enhancing the performance of cervical cell classification. However, most current methods have overlooked two major concerns: the pattern variations in cervical cells caused by data acquisition process and the misclassification of cervical cells with similar pathological properties. To address these issues, we develop a new cervical cell classification framework that incorporates supervised contrastive learning with CNN. Methods: To simulate the pattern variations of cervical cells, we first adopt data augmentation to generate multiple views of cell images, which are then fed into three main components of the model, including the encoder, contrastive, and classification modules. Moreover, we design a hybrid loss to jointly train the model to learn more robust cell representations by introducing the supervised contrastive loss into the traditional classification loss. Findings: Experimental results on four cell image datasets demonstrate that the proposed method achieves better performance than the competing methods. Our hybrid loss yields the highest F-score, improving the classification and supervised contrastive losses by 3.3% and 2.6%, respectively, further illustrating the superiority of our method in cervical cell classification. Novelty: Through the combination of supervised contrastive learning and traditional classification, our method obtains better representations from cervical cell images, enhancing the model robustness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".