Multi-Scale Hematological Image Analysis for WBC Classification via GNNs
Bibliographic record
Abstract
Traditional deep learning models, like the Convolutional Neural Networks (CNNs) fails in domain shift due to inconsistencies arising due to variances in staining and imaging techniques. This study attempts to use Graph Neural Networks (Graph Neural Networks) for the classification of WBCs given their ability to detect structural and relational patterns from microscopic images. Now, to avoid redundancy and allow generalization, out of 47 datasets six datasets plus the combined set were chosen. The technique includes image preprocessing, normalization, resizing to$224 \times 224$pixels, and data enhancement to promote more robustness in the model. The GNN model was trained and tested utilizing accuracy metrics, precision metrics, recall, F1-score, confusion matrices, and ROC curves. Comparative analysis indicated that by training the model on a heterogeneous dataset, enhances model generalization, and identifying spatial dependencies in the WBC images was done better than with CNNs. These results demonstrate the potential of Graph Neural Networks in AI-based hematological diagnostics to further strengthen medical image analysis.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".