Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning
Bibliographic record
Abstract
Accurate detection, localization, and staging of breast cancer lymph node metastases are critical for guiding treatment decisions and predicting patient outcomes. This study presents a selective neighborhood attention-based deep learning framework that combines nuclei-level features with high-level tissue embeddings to detect, annotate and stage breast cancer metastases in whole-slide images (WSIs) of lymph node biopsy specimens precisely. The proposed framework leverages a dual-path feature extractor, incorporating both nuclei segmentation/classification outputs and transformer-based tissue features, alongside a dynamic attention mechanism that selects and emphasizes neighboring patches based on similarity to the target patch. Experimental results on the CAMELYON16 test set demonstrate high performance in patch-level tumor detection, with sensitivity of 96.2 ± 1.5%, precision of 95.3 ± 2.4%, and an F1-score of 95.7 ± 3.1%. The model achieves accurate tumor boundary delineation, evidenced by a Dice score of 90.5 ± 2.0% and a Jaccard index of 82.6 ± 0.8%, along with a lesion-level free-response receiver operating characteristic (FROC) score of 84.6 ± 2.8%. Additionally, the slide-level classification achieves an area under the receiver operating characteristic (ROC) curve (AUC) of 0.96 ± 0.01, highlighting the system's strong diagnostic capability. Out-of-distribution evaluation on the CAMELYON17 dataset confirms the framework's generalizability, yielding an F1-score of 87.0 ± 1.8% at the patch level and an AUC of 0.88 ± 0.03 at the slide level. Furthermore, the proposed model achieves a kappa score of 0.94 ± 0.02 for automated pN-staging at the patient level, indicating near-expert concordance in detecting and classifying the extent of nodal metastasis. Ablation analyses underscore the importance of incorporating nuclei-based features and selective neighborhood attention, with noticeable performance degradation observed when either element is removed. By integrating cellular-level insights with tissue-level contextual information, the proposed framework replicates key aspects of human pathological assessment effectively and shows promise as a decision-support tool in the era of digital pathology.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".